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2021 ACE Breeding Inspection and Assessment tour

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ACE ASSESSMENT TOUR – 2023

We had the pleasure of having the ACE Assessment Tour team visit Finch Farm last week to assess our current crop of home bred foals. For those unfamiliar with the process, each foal is marked on a range of factors focusing on the movement and confirmation of the horse. The assessment is performed by the Internationally respected assessor selected by the Australian Continental Equestrian Group Inc. To read more about the organisation and what they do, click here:

ACE Group Inc. – Achieving Your Dreams

This year, we had the pleasure of meeting Jens Wehrmann, who took the time to travel from Germany to tour and assess our Australian horses. A huge thank you to Jens, Ingrid and Jan – it was an incredibly hot and humid day and your patience and dedication didn’t go unnoticed.

We showcased 18 foals. Of the foals presented, 9 received a level of Elite (80% and over and the highest award achievable) and 7 foals awarded a ‘Premium’ result with scores between 78% to 79.9%.

We had some incredible feedback on our breeding program as a whole, which is extremely exciting for the future of our Finch Farm progeny. For any enquiries on the following foals, please contact David on 0412 147 838.

Read on to meet the ELITE Finch Farm foals of 2023 .

Overall Score – ELITE – 86.75 % – Chestnut Filly – Current overall leader – 2023 QLD ACE assessment Tour

Sire – Clarity GNZ ( Clearway / Casall / Lux Z / Calypso II / Malteser )

Dam – Finch Farm Amelia ( Alimo / Bicentenary xx / Bold Lip xx )

Overall Score – ELITE – 84.25% – Bay Filly

SIRE – Massimo Quality ( Mylord Cathargo / Quidam de Revel / Galoubet A / Night and Day xx / Furioso xx )

DAM – Finch Farm Lulu (Casall / Lire News xx / Bold Clipper xx)

Bay Colt – Overall score – ELITE – 83.75%

SIRE – Corvetto ( Cornet Obolensky / Claudio / Landgraf I / Calypso I / Farnese )

DAM – Finch Farm Twiggy ( Calgary GNZ / Finch Farm Nikko / Indoctro / Stirling Liberty / Contact / Souvenir )

Overall score – ELITE – 83.5% – Bay Colt

DAM – Finch Farm Lucinda ( Charlemagne Ego Z / Biscay Wonder xx / Baptism xx / Smokey Eyes xx)

Overall score – ELITE – 81.5% – Bay Filly

SIRE – Clarity GNZ ( Clearway / Casall / Lux Z / Calypso II / Malteser )

DAM – Finch Farm Odette Quality ( Kannan / Saphir Rouge II / Jalisco B / Nankin )

Overall score – ELITE – 81.75 – Chestnut Colt

SIRE – Calgary GNZ ( Casall / Calato / Silvester / Frivol xx / Ramzes ox )

DAM – Finch Farm Averil ( Animate / Taoiseach xx / Amyntor xx / Pakistan II )

Overall Score – ELITE – 82.5% – Liver Chestnut Filly

SIRE – Calgary GNZ (Imp) ( Casall / Calato / Silvester / Frivol xx / Ramzes ox ) stamm 173

DAM – Oaks Taloona ( Tinka’s Boy / Converter / Kannan / Zeus / Furioso xx )

Overall Score – ELITE – 82.75% – Chestnut colt

DAM – Noblewood Park Cassini’s Girl (Imp) ( Cassini II / Ladalco / Joost / Abgar xx )

Overall Score – ELITE – 80% – C hestnut colt

DAM – Finch Farm Flo ( Finch Farm Nikko / Northern Congress / Arnhem / Flaneur )

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• v.10; 2022 Dec

Psychometric evaluation of an Adverse Childhood Experiences (ACEs) measurement tool: an equitable assessment or reinforcing biases?

Xiaohan mei.

1 California State University, Los Angeles, 5151 State University Dr, Los Angeles, CA 90032 USA

2 Washington State University, Pullman, WA 99164-2520 USA

3 Shenyang Open University, Shisiwei Rd, Heping District, Shenyang, Liaoning 110003 China

4 Beijing Hospital, 1 Dahua Rd, Dongcheng, Beijing, 100051 China

5 Shenyang Maternity and Child Health Hospital, 41 Shenzhou Street, Shenhe District, Shenyang, 110000 China

Yang-Hong Huang

6 Shenyang Women and Children’s Hospital, No. 87 Danan street, Shenhe District, 110000 Shenyang, China

Jianhong Liu

7 University of Macau, Avenida da Universidade Taipa, Macau, 999078 China

Associated Data

The datasets analyzed during the current study are available in the 2019 Behavioral Risk Factor Surveillance System (BRFSS; http://www.cdc.gov/brfss/ ).

Utilizing Adverse Childhood Experiences (ACEs) measurement scales to assess youths’ adversities has expanded exponentially in health and justice studies. However, most of the ACEs assessment scales have yet to meet critical psychometric standards, especially for key demographic and minority groups. It is critical that any assessment or screening tool is not reinforcing bias, warranting the need for validating ACEs tools that are equitable, reliable and accurate. The current study aimed to examine the structural validity of an ACEs scale. Using data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS), which collected of 97,314 responses collected from adults across sixteen states. This study assessed the psychometric properties and measurement invariance of the ACEs tool under the structural equation modeling framework.

We found the 11-item ACEs screening tool as a second-order factor with three subscales, all of which passed the measurement invariance tests at metric and scalar levels across age, race, sex, socioeconomic status, gender identity, and sexual orientation. We also found that minority groups experienced more childhood adversity with small effect size, with the exception of the gender identity.

The ACEs measurement scale from the BRFSS is equitable and free from measurement bias regardless of one’s age, race, sex, socioeconomic status, gender identity, and sexual orientation, and thus is valid to be used to compare group mean differences within these groups. The scale is a potentially valid, viable, and predictive risk assessment in health and justice and research settings to identify high-risk groups or individuals for treatments.

Background/Rationale

A relatively recent public health and justice concept, the adverse childhood experiences (ACEs) scale, (Anda et al., 2010 ; Ford et al., 2020 ), is defined as “potentially traumatic events that occur in childhood (Centers for Disease Control and Prevention, 2022 ).” The American Academy of Pediatrics’ (AAP’s) policy statement encourages pediatricians to screen ACEs for the toxic stress of children and adolescents early (Committee on Psychosocial Aspects of Child and Family Health et al., 2012 ). ACEs have been found to be associated with increased physical and mental illness through the engagement of health-risk behaviors (Baldwin et al., 2021a ; Centers for Disease Control and Prevention, 2022 ; Hughes et al., 2021 ), and has been linked to $748 billion in related health costs (Bellis et al., 2019 ). Recently, the COVID-19 pandemic worsened the youths’ ACEs and toxic stress (Ortiz et al., 2022 ), as counties in the world implemented lockdown policies, closed schools, and disrupted governmental and private services, which left many children unprotected. Several countries started implementing ACEs screening through either universal (e.g., well-childcare) or targeted platforms (e.g., pediatricians), but the ACE-induced health issues are unlikely to be neutralized without the appropriate treatments and interventions; however, the limited studies suggested that screening for ACEs improves adversity identification and receiving community-based services.

ACE assessment, structural validity and measurement invariance/bias

The bridge between adversity identification, risk assessment and intervention/treatment referral or resource allocation is the ACEs screen and assessment tools (Gordon et al., 2020 ). Despite the utility of the ACE assessment or screening, no instrument has accumulated sufficient psychometric evidence to demonstrate its superiority in terms of its predictive accuracy and economic viability 1 (Loveday et al., 2022 ). Despite its utility, there are many methodological concerns of the ACEs assessment remains to be resolved (Holden et al., 2020 ). One and the most fundamental methodological concern is the how well ACEs are assessed or the validity of the assessment itself (Holden et al., 2020 ). This concern is two-fold. First, “what is the underlying factor structure of childhood adversities?” and second, “does the instrument demonstrate measurement invariance,” or “is the instrument equally appropriate for assessing adversity from a variety of individuals? (Holden et al., 2020 , p.169)”

While the first question pertains to the ACEs assessment of structural validity, the second question deals with measurement bias imbedded in the assessment instrument itself, which would produce biased estimation for key demographic groups. While there are twenty different versions of the ACEs assessment scales, ranging from 8 to 70 items per instrument, only four studies explored the structural validity of the ACEs instruments, among which only three studies investigated the measurement invariances/bias across certain demographic groups, such as age and sex (see Holden et al., 2020 ).

Briefly, when evaluating the measurement invariance, researchers must provide at least three levels or tiers (configural, metric and scalar) of evidence to claim the assessment instrument is not biased toward any of the subgroups (Ford et al., 2014 ). Nevertheless, one the of ACEs assessment scale validation study claimed that they achieved measurement invariance, but in fact the measurement invariance failed at the scalar level for youth gender groups (girls and boys) (Meinck et al., 2017 ). The other two studies tested and passed the measurement invariance of two different version of ACEs scales (from the Panel Study of Income Dynamics and the BRFSS project) across gender and age (Olofson, 2018 ; Ford et al., 2014 ). Unfortunately, the equality of the assessment based on ones’ group memberships, especially for social disadvantaged minority groups yet to be tested and validated (i.e., sexual and gender minorities). There is evidence that disadvantaged, or minority groups are more likely to suffer various types of early childhood adversities (Centers for Disease Control and Prevention, 2022 ). Although disadvantaged groups might score higher on the ACEs assessment scales, the differences found between disadvantaged and non-disadvantaged groups might be artificial and could only be the product of measurement biases imbedded in the assessment itself due to the lack of measurement invariance validations.

Health equity tourism

Since the seminal research on ACEs (Felitti et al., 1998 ), the ACEs studies on justice and health outcomes have proliferated (Baglivio et al., 2014 ). Researchers recognized that the interrelationship between the ACEs and social inequalities (McEwen & Gregerson, 2019 ; Racine et al., 2022 ). However, in order to address the ACEs among the youth’s population through preventive measures, both the clinicians and researchers must have the assessment tool to accurate measure the underlying ACEs constructs across all key demographic groups.

Lett and colleges defined ‘health equity tourism’ as researchers jumped on the bandwagon of equity research for pursuing health and justice publications or fundings without investigating the sources of the resource of inequality (i.e., structural racism) ( 2022 ). The ACEs research to date have not resolve the measurement methodology issues (i.e., measurement invariance) for ACEs assessment, which questions the validity of some research findings of all ACEs studies, especially for minority groups (Holden et al., 2020 ). Without empirical evidence that an ACE assessment scale is unbiased across disadvantaged groups, the ACE assessment might, instead of addressing and improving, reinforce social inequality because the assessment contents and items might be inappropriate to assessing adversity for the minority groups.

Current study

Therefore, to fill the gap in the literature regarding the lack of critical evaluation of the ACEs assessment scales used by researchers and clinical professionals, we attempt to investigate the structural validity of the ACEs scale from the BRFSS while evaluating the measurement bias for the vulnerable and marginalized populations, such as racial ethnic minorities, people with lower socioeconomic status, sexual and gender minorities. We select the ACEs scale from the BRFSS because it is one of the few promising instruments that has accumulated considerable psychometric evidence (Holden et al., 2020 ). This instrument can also be used in a self-report format, which has been used to generate a national representative sample to obtain external validity. This instrument economic with only has 11 items, which can be easily to be adopted and incorporated into many research projects without burden the participants. Therefore, in this study we attempt to validate the structural validity and the lack of measurement bias of this ACEs assessment scale from the BRFSS with a national representative sample. We hypothesized that this ACEs assessment scale are free from measurement biases across all major minority groups.

Study sample

In this study, we evaluated the internal latent structure (structural validity) and the measurement bias of the ACEs scale using data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS; http://www.cdc.gov/brfss/ ). The BRFSS was initiated by the Centers for Disease Control and Prevention (CDC) in 1984. According to the BRFSS Data User Guide ( 2013 ), state health departments, assisted by the CDC, conducted yearly telephone surveys to collect data with standard protocols on adults’ risk behaviors, preventive health practices, and health status. For each year, the annual sample contains more than 4,000 telephone interviews that were conducted for each state. The BRFSS used a stratified random sampling approach with a weighting protocol, ensuring the generalizability and representativeness of many demographic characteristics, such as sex, age, race and education. We used the 2019 BRFSS sample ( N  = 97,314) from the BRFSS, which collected ACEs assessments from sixteen states, including Alabama, Delaware, Florida, Indiana, Iowa, Michigan, Mississippi, Missouri, New Mexico, North Dakota, Pennsylvania, Rhode Island, South Carolina, Tennessee, Virginia, West Virginia, and Wisconsin. The sample characteristics are reported in Table  1 .

Sample descriptive ( N  = 97,314)

Measurements

The outcome measure is the ACEs, which contains eleven binary and ordinal items assessing whether an individual suffered various types of adverse childhood abuses, such as physical, verbal, and sexual abuse, as well as experienced any traumatic events, such as the parental incarceration and separation. The full item descriptive statistics were reported in Table  2 .

ACEs Item descriptive statistics ( N  = 97,314)

When testing ACEs’ measurement bias, we used six nominal grouping variables, including age, race, sex, socioeconomic status, sexual identity, and sexual orientation. Age was operationalized into six categories, including “18–24,” “25–34,” “35–44,” “45–54,” “55–64” and “65+.” The biological sex was operationalized as either “male” or “female.” Income was operationalized six categories, including “less than 15,000,” “15,000 to less than 25,000,” “25,000 to less than 35,000,” “35,000 to less than 50,000,” “50,000+,” 2 and “Don’t know/Not sure/Missing.” Race was operationalized into five categories, including “white only”, “non-Hispanic,” “black only, non-Hispanic,” “other race only, non-Hispanic,” “multiracial, non-Hispanic,” “Hispanic.” Sexual orientation is measured as “straight” and “others”, which include gay, bisexual, something else, and I don’t know the answer. Sexual identity was measured as “not transgender” and “transgender.”

Analytical Strategy

We first conducted an Exploratory Factor Analysis (EFA) to discover the underlying factorial pattern. Second, we conducted a sequential Multi-group Confirmatory Factor Analysis (MGCFA) to confirm the suggested factorial pattern. We extracted a second-order factor through higher-order modeling when we identified that the factors shared a substantial amount of common variance (Chen et al., 2005 ; Putnick & Bornstein, 2016 ). Once the internal latent structure of the ACEs was identified, we tested three essential forms of measurement bias or invariances, including configural, metric, and scalar, across all the group memberships (Schmitt & Kuljanin, 2008 ). Moreover, we reported the latent mean difference (i.e., true mean difference) represented by the Cohen’s d (Fritz et al., 2012 ) across all six group memberships. We followed the interpretations provided by Cohen (Cohen, 1988 ) when evaluating the effect size of the mean difference, ranging from small (0.20), medium (0.50), and large (0.80) effect size. In addition, we performed a common factor model when measurement invariance was achieved at all three invariance levels.

We followed guidelines for testing sequences of measurement invariance and higher-order factors (Chen et al., 2005 ; Rudnev et al., 2018 ). The fixed factor approach was used and we followed the model specification and identification suggestions by previous studies (Byrne & Stewart, 2006 ; Millsap & Yun-Tein, 2004 ). We performed omnibus tests for higher-order modeling and measurement invariance tests and conducted further testing when the omnibus tests failed (Little, 2013 ). The Weighted Least Square Mean and Variance Adjusted (MLSMV) is the preferred estimator because the items are categorical/ordinal and polytomous. The ‘Theta’ parameterizations is selected because it allowed us to test all forms of measurement invariances (Muthén & Asparouhov, 2002 ). Because the items are categorical, we conducted all tests within the Item Factor Analysis (IFA)/Item Response Theory (IRT) framework (Thomas, 2011 ). The missing data are handled with the full information maximum-likelihood (FIML) approach with MLSMV estimator when there is non-substantial missing at random data (Asparouhov & Muthen, 2010 ). The FIML is a superior method than the listwise deletion, pairwise deletion and imputation approaches (Enders & Bandalos, 2001 ).

Next, we computed the coefficient omega (ω) to evaluate the construct reliability of the G-factor and subscales. Using the Omega coefficient is advantageous over Cronbach’s Alpha because it assumes a parallel construct measurement structure (Deng & Chan, 2017 ; Geldhof et al., 2014 ; Nájera Catalán, 2019 ) and it enables researchers to accurately evaluate the construct reliability for higher-order factors (Nájera Catalán, 2019 ). A threshold of 0.65 for multidimensional (higher-order) and 0.80 for unidimensional (first-order) measures were used as thresholds to determine the ‘acceptable’ level of construct reliability (Nájera Catalán, 2019 ).

When evaluating the goodness of the EFA model, we followed the industry standard which considers both theory and the empirical evidence, such as the Kaiser-Guttman rule and goodness of fit, to determine the number of factors (Brown, 2015 ). For item loadings and cross-loadings, we also followed Comrey and Lee’s (Comrey & Lee, 1992 ) guidelines that the strength of the loadings and cross-loadings range from poor (.32), fair (.45), good (.55), very good (.63) or excellent (.71) fit. When evaluating the goodness of the CFA models, we compared item and factor loadings/cross-loadings with industry-standard loading thresholds of poor (.32), fair (.45), good (.55), very good (.63), and excellent (.71) (Tabachnick et al., 2007 ). Model fit is ‘acceptable’ if the Comparative Fit Index (CFI)/Tucker Lewis Index (TLI) are equal or greater than .90 and the Root Mean Square Error of Approximation (RMSEA) is equal/less than .08. The model fit is ‘good’ when CFI/TLI are equal or exceed .95 and the RMSEA is equal/less than .05 (Brown, 2015 ; Little, 2013 ). Models were evaluated with constraints added to each additional and progressive model for higher-order and group invariance tests. Higher-order models and those with additional measurement invariance constraints were retained if the ∆CFI and ∆TLI values were equal/less than .01, indicating that the nested higher-order modeling or additional measurement invariance constraints did not produce any detrimental effect on the models (Little, 2013 ).

We identified that the ACEs scale was a second-order model with three subscales. The EFA suggested a two-factor model because there were two Eigenvalues above 1, yet the SRMR model fit was not ideal (CFI = .985, TLI = .976, RMSEA = .027, SRMR = .055). Also, compared with a 2-factor model, the 3-factor model made significant improvement in all models’ fit indices with ∆CFI and ∆TLI above .10 (CFI = .997, TLI = .993, RMSEA = .015, SRMR = .023). The assessment content is aligned with the suggested factorial pattern in the 3-factor model, assessing three sub-types of ACEs: household dysfunction , emotional/physical abuse , and sexual abuse .

Next, we retained the three-factor model and subjected the measurement model to measurement invariant tests and higher-order model tests. As a result, we successfully exacted a second-order model, as the second-order model did not produce detrimental model fits (with ∆CFI and ∆TLI below .010) compared to the measurement model across all six grouping models. Also, as shown in Table  3 , the second-order model passed all three levels of invariances (i.e., configural, metric, and scalar) for all six groups as the ∆CFI and ∆TLI did not exceed .10 for all models (Table  4 ). Finally, we conducted a common factor model, combing all the groups, and the final model fits exceeded all thresholds to be at least considered “acceptable” (CFI = .986, TLI = .985, RMSEA = .021, SRMR = .066). The reliability of the ACE scale reached an acceptable level of reliability, which passed the threshold of .65 for multidimensional measures (ω = .906). We provided a visual illustration of the ACE final model in Fig.  1 .

Measurement invariance tests across age, race, sex, income, sexual identity, and sexual orientation groups

a First-order factor loadings were set to be equal within groups to obtain an over-identified model

b First-order within-factor items are constrained to be equal but allowed to vary across groups

Latent mean difference for ACEs across age, race, sex, income, sexual identity, and sexual orientation

a The Reference Group

Final model of Adverse Childhood Experiences (ACEs)

Now, we present the true score differences across six group memberships in Table  4 . We found that compared people aged between 18 and 24, people aged between 45 and 54 ( d  = .02, p  < .05), 55 and 64 ( d  = .04, p  < .001), and people who are above the age of 65 ( d  = 0.14, p  < .001) reported statistically lower ACEs scores. Compared to non-Hispanic whites, Black only ( d  = .08, p  < .001), non-Hispanic multiracial ( d  = .15, p  < .001), and Hispanic ( d  = .06, p  < .001) scored significantly higher. Females scored higher on ACEs than male participants ( d  = .08, p  < .001). Compared to people whose income was less than $15,000, people in higher-income groups scored significantly lower ACEs ( d  = .04 − .16, p  < .001). Compared to heterosexual people, sexual minorities scored significantly higher ( d  = 0.18, p  < .001). Gender minorities (i.e., people who identified as transgender) scored higher than people who are cisgender ( d  = .18, p  > .05), yet the mean difference is not statistically significant. With Cohen’ d less than 0.20, all the statistical differences we found were small.

The current study made several contributions. First, consistent with a previous study that used an early version of the BRFSS data (D. C. Ford et al., 2014 ), we found the CDC’s ACEs Scale contains three subscales, including household dysfunction , emotional/physical abuse , and sexual abuse . Compared to the ACEs total score, each of its subscales has fewer items and, therefore, less variation and range. We advocate for using the composite scores of the ACEs scale with all items for screening instead of using three subscales separately because the common variance of the three subscales can be explained by one underlying factor, namely the ACEs, through second-order modeling. Given each of the three subscales has a limited number of items and range, and the utility of the subscales is yet to be fully explored, greater weight should be given to the entire ACE assessment in clinical practice for screening and public health research. Once the screening is completed, clinical practitioner could use more extensive and comprehensive tools to fully assess youths’ ACEs, and which type or subtype of the ACEs is the most stressful and traumatic for the youths. The finding of the current study demonstrated the ACEs assessment instrument can provides the clinicians a potentially promising, viable and economic screening tool to assess ACEs.

Second, the ACE scale passed the three levels of invariance tests (i.e., configural, metric, and scalar) across six group memberships, indicating that the ACEs assessment is equitable and free from measurement bias regardless of one’s age, race, sex, socioeconomic status, sexual identity, and sexual orientation. In other words, the ACEs scale is a valid screening tool to assess the group mean differences within these groups.

Third, since the ACEs scale is invariant, we used it to examine the group differences in age, race, gender, income, sexual identity, and sexual orientation. We found evidence suggesting that as one’s age increases, their ACEs scores decrease, such as significant relationship no longer holds for people were 45 and older. Given that the data were collected through the participants’ memory, there was an increased risk of recall bias for people aged 45 and older, suggesting that using the ACEs might not be suitable for clinical and research use if the individuals are older than 45-years-old because of the recall bias.

Furthermore, previous studies on group differences, such as gender, racial, and sexual minorities group differences, in ACEs often examine different types of ACEs separately (Andersen & Blosnich, 2013 ; Fang et al., 2016 ; Lee & Chen, 2017 ), and this study filled this gap by examining group differences in the ACEs as a single construct. Consistent with the previous findings, we found that non-Hispanic black, Hispanic, and non-Hispanic multiracial people reported higher ACEs, which indicated that people of racial minority experienced more adverse childhood experiences than white people. Similar to a previous study that females were at more risk of multiple types of ACES (Fang et al., 2016 ), females in this study reported higher ACES than males. In addition, we found that people’s socioeconomic status is significantly and negatively associated with ACEs.

Moreover, gender minorities reported higher ACEs than people who are cisgender. However, such a relationship is not statistically significant. Also, sexual minorities scored higher than heterosexual people. A possible explanation is that the difference and disparity can be attributed to structural racism (Dougherty et al., 2020 ). Alternatively, multi-level (micro and macro) and multisystem (family and neighborhood) characteristics could also explain said disparities. Unfortunately, without adequately designed research, the challenges of explaining the health disparity cannot be properly investigated in the current study (Jeffries et al., 2019 ).

We found that the effect sizes of the reported group differences are small. Overall, the findings support the theory that the vulnerable population, including women, young adults, racial ethnic minorities, people lower on the socioeconomic ladder, and LGBT groups, suffered more adverse, traumatic, physical, psychological, and sexual abuses in their early lives. Due to the limited scope of this research, we did not examine the intersectionality of the disadvantaged groups could experience more ACEs. Given the current finding, it is reasonable to speculate that the youths belong to multiple disadvantaged groups could have experiences more ACEs than non-disadvantaged population.

The current public, justice and health system might not have the capacity to address the needs for all individuals (McLennan et al., 2020 ). Fortunately, with this validated ACEs, it is possible to accurately identify these high-risk vulnerable individuals. Also, the traditional prevention strategy framework recognizes that children with higher risk should be prioritized to receive prevention treatments (Brennan et al., 2020 ). The traditional prevention strategy often consider race, sex, sexual orientation, sexual identity, socioeconomic status and age independently and therefore fails to address the multiple intersecting needs of the individuals (Qureshi et al., 2022 ). While the finding of this research calls for critical examination of the underlying structure and factors that contributed to the disparities and how the prevention programs could be tailored to multiple intersecting higher-than-average needs of the minority populations who are likely belong multiple disadvantaged groups.

Limitations

The current study has several limitations. First, not all states collected data on ACEs in the 2019 study. Although the sample is large, the generalizability to the entire U.S. population of the findings remains to be further validated. Even with more states’ participation, the generalizability of the result is still confined to the U.S. and North America. Second, due to the limited scope, the predictive accuracy of the CDC’s (11-item) version of the ACEs (both the composite total and subscales’ score) remains to be further tested in future research across various justice and health outcomes as well as across various groups of children as the previous research demonstrated the lack of prediction precision for health issues (Baldwin et al., 2021b ). Future research could maximize the ACEs predictive accuracy by using more sophisticated weighting schemes based on its empirical relationship with various health outcome interests to further support the screening practices (Holden et al., 2020 ). Future research could use longitudinal instead of cross-sectional data to validate the precision of the ACE assessment when used to predict or explain justice or health outcomes, such as illegal substance abuse, chorionic disease, and mental health. Researchers may even consider a more complicated model to account for the mediation or neutralizing effect of the positive childhood experience on ACEs (Ortiz et al., 2022 ).

Third, the data probably underestimated the prevalence of ACEs especially for sexual minorities because of the housing insecurity or instability (Tran et al., 2022 ). It is difficult to estimate the prevalence of homeless because of the heterogeneity of the samples in previous studies. Yet, it is evidentiary that sexual minority in general at disproportionately higher risk of homeless (Corliss et al., 2011 ). Therefore, sexual minorities are likely underrepresented in the 2019 BRFSS which is a household sample. The low frequency of gender minority youths in our sample might produce the non-significant group mean difference between gender identity groups. Future research should reinvestigate the ACEs difference between such groups with larger samples.

Also, the data were collected from adults’ recollection of the memory and therefore further underestimated the prevalence, especially for the older population (Tran et al., 2022 ). Next, the utility of the CDC’s ACEs screen instrument’s forecasting utility remains to be validated among the youth and children’s populations. Hence, longitudinal research tracking youths’ health development over time might offer more definitive evidence (Lacey et al., 2022 ). In addition, this study used EFA to identify subscale of the ACE and used (Multigroup) CFA to confirm the ACEs constructs with the same sample. Although the results are unlikely to differ from the current finding, future research could further revalidate the ACEs scale from the BRFSS with new data.

Last, researchers have identified that most of the current ACEs (including CDC’s version) did not follow the scale creation processes and standards and therefore lacked construct validity. Although the current research offered convincing evidence to support the potential utility of using it as a screening instrument, it still lacks content validity because of the limited items ( n  = 11) and measured types of adversity (Holden et al., 2020 ). While using existing tools with more screening content (Brennan et al., 2020 ), or expanding the content validity of the ACE might be beneficial, the CDC’s version of ACEs might be offered to health platforms as an economically viable screen tool, upon demonstrating its preferred level of prediction precision for various health outcomes. The limitation of the range and assessment content of the tool might be further mediated with more sophisticated psychometric methods, which allow clinicians and researchers to produce weighted latent factor scores (Grice, 2001 ). Applying more sophisticated methods from machine learning techniques might offer a sizeable boost to the predictive accuracy in clinical practices, which has been used in justice settings to predict health outcomes, such as substance abuse or drug crimes (Hamilton et al., 2021 ). Once the screen is completed and high-risk individuals are identified, a complete or a more comprehensive ACE clinical assessment might be employed to toxic stress risk (Harris, 2020 ).

Acknowledgements

We thank Lijie Jia for the assistance with arranging research meetings for the team, actively involving the conceptualization and execution of the research projects. We thank you for your interest in learning how health and justice research is conducted and reviewed. We thank Dr. Zachary Hamilton for editing the paper and providing insightful feedback and comments regarding how to improve the manuscript.

Authors’ contributions

XM and JL were involved in the conceptualization of the paper; both analyzed the data and wrote parts of the methodology and results. ZL, SH, LL, YH and JL wrote the original draft. All authors revised, read and approved the final manuscript.

Not applicable

Availability of data and materials

Declarations.

This study used a publicly available dataset and was exempted by the Institutional Review Board (IRB) of the authors’ university.

The authors declare that they have no competing interests.

1 Economic viability is referred to as whether the tool is short but reliable and valid that can be used to assess ACEs in relatively cost-effective fashion.

2 We recognize this categorization which does not further distinguished people who are in higher socio-economic status (i.e., maybe 80,000 + or 150,000+). Unfortunately, this is the one of the limitations that this study which used a secondary data (i.e., the BRFSS) for analyses.

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Contributor Information

Xiaohan Mei, Email: ude.aletatslac@4iemX .

Jiayu Li, Email: [email protected] .

Zhi-Shu Li, Email: [email protected] .

Shun Huang, Email: moc.liamg@990gnauhnuhs .

Li-Li Li, Email: [email protected] .

Yang-Hong Huang, Email: [email protected] .

Jianhong Liu, Email: om.ude.mu@uilj .

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What Shapes Health

Take the ace quiz — and learn what it does and doesn't mean.

Laura Starecheski

An ACE score is a tally of different types of abuse, neglect, and other hallmarks of a rough childhood. According to the Adverse Childhood Experiences study, the rougher your childhood, the higher your score is likely to be and the higher your risk for later health problems. You can take the test below:

So, you've got your score. Now what?

First, remember that the ACE score isn't a crystal ball; it's just meant as guidance. It tells you about one type of risk factor among many. It doesn't directly take into account your diet or genes, or whether you smoke or drink excessively — to name just a few of the other major influences on health.

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To learn more, check the CDC's ACE Study website . You'll find, among other things, a list of studies that explore the ways adverse childhood experiences have been linked to a variety of adult conditions, ranging from increased headaches to depression to heart disease.

Remember this, too: ACE scores don't tally the positive experiences in early life that can help build resilience and protect a child from the effects of trauma. Having a grandparent who loves you, a teacher who understands and believes in you, or a trusted friend you can confide in may mitigate the long-term effects of early trauma, psychologists say.

"There are people with high ACE scores who do remarkably well," says Jack Shonkoff, a pediatrician and director of the Center on the Developing Child at Harvard University.

Resilience, he says, builds throughout life, and close relationships are key. Recent research also suggests that for adults, "trauma informed" therapy — which can center on art, yoga or mindfulness training — can help.

Three Types of ACEs

Source: Centers for Disease Control and Prevention

Credit: Robert Wood Johnson Foundation

How best to find and help kids who are experiencing abuse and neglect right now?

Child psychologist Hilit Kletter , of Stanford University's School of Medicine, says that to spot these children, she looks for visible signs of stress to understand what might have happened to them and how best to intervene. Some kids have nightmares or recurring thoughts of a stressful event, she says, or may re-enact the trauma through play. Or the child may seem distracted or withdrawn.

"This will come out at school," Kletter says. "Teachers will tell parents [their child] seems to be in a daze in the classroom, not paying attention."

ACEs Increase Health Risks

According to the Adverse Childhood Experiences study, the rougher your childhood, the higher your score is likely to be and the higher your risk for various health problems later.

Kletter says reactions to trauma are sometimes misdiagnosed as symptoms of attention deficit hyperactivity disorder, because kids dealing with adverse experiences may be impulsive — acting out with anger or other strong emotions.

"It's something that's very common in trauma: difficulty in regulating emotions and behavior," she explains. "That's why a lot of these kids get in trouble with the classroom."

People With Low Incomes Say They Pay A Price In Poor Health

Shonkoff's research center at Harvard tests interventions that can build resilience in kids who are growing up with adverse experiences — not just problems in the family, such as those the ACE study investigated, but also trauma stemming from poverty, for example, or from the chronic stress of racial or gender discrimination.

To bolster parents, the Harvard team is testing interventions right now that use video coaching to show moms and dads how to engage their babbling infants, using sounds and facial expressions in a style Shonkoff calls serve and return .

Shonkoff says these early interactions — a kind of conversation — have been shown to help children with later learning and literacy. Even more important, they boost kids' resilience, by helping them build secure attachments with caring adults. Research suggests that just one caring, safe relationship early in life gives any child a much better shot at growing up healthy.

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Clinical Assessment & Treatment

Identifying ACEs and risk of toxic stress to inform treatment

Screening for ACEs and responding with evidence-based interventions and trauma-informed care can prevent and treat toxic stress to improve patients’ physical and mental health.

Screening for ACEs involves assessing for exposure to adversity (using the ACE score), clinical manifestations of toxic stress (ACE-Associated Health Conditions), and protective factors. Clinical teams should use the ACEs and Toxic Stress Risk Assessment Algorithm to assess whether a patient is at lower, intermediate, or higher risk for having a toxic stress physiology.

Learn more about screening > 

Gathering Information

Clinicians routinely gather information from patients about their medical history, family history, and any specific health concerns during the primary care visit. Incorporating ACEs and toxic stress into the conversation involves reviewing the patient’s ACE screening results, asking about protective factors – including toxic stress mitigation strategies – that may be present, and taking note in the physical exam of any neurologic, endocrine, metabolic, or immune findings that could be associated with ACE-Associated Health Conditions.

Conducting the Clinical Assessment

Using the information gathered – including the ACE score and indications of ACE-Associated Health Conditions – clinicians use the ACEs Aware ACEs and Toxic Stress Risk Assessment Algorithm to assess the patient’s risk for toxic stress. Taken together, the ACE score and ACE-Associated Health Conditions indicate if a patient is likely to be at lower, intermediate, or higher risk for toxic stress physiology.

Developing the Clinical Response

The clinical response is informed by the patient’s risk for toxic stress in the clinical assessment and the protective factors that are in place, including the presence of toxic stress mitigation strategies.

The clinical response involves providing the following based on the level of risk of toxic stress:

• Patient education about toxic stress and its likely role in ACE-Associated Health Conditions;
• Clinical interventions and support services, including evidence-based toxic stress mitigation strategies (which starts with the clinician but may be supplemented by the broader clinical team within the clinic/system and/or by community-based organizations); and

The clinical response to identification of toxic stress should include:

1. Applying principles of trauma-informed care, including establishing trust, safety, and collaborative decision-making.

2. Supplementing usual care for ACE-Associated Health Conditions with patient education on toxic stress and discussing strategies that can help regulate the stress response, including:

• Supportive relationships, including with caregivers (for children), other family members, and peers
• High-quality, sufficient sleep
• Balanced nutrition
• Regular physical activity
• Mindfulness and meditation
• Experiencing nature
• Mental health care, including psychotherapy or psychiatric care, and substance use disorder treatment, when indicated

3. Validating existing strengths and protective factors.

4. Providing referrals to patient resources or interventions, such as educational materials, social workers, school agencies, care coordination or patient navigation, and community health workers.

5. Following up as necessary, using the presenting ACE-Associated Health Condition(s) as indicators of treatment progress.  

Increasing ACEs awareness and training has never been so important. Together, we can significantly improve the health and well-being of children, adults, and families to help save lives.

See the ACE Screening Implementation How-To Guide on Preparing the Clinical Response   for more detailed information about prevention, the clinical assessment, and treatment of toxic stress. Find additional patient & family education handouts in the ACEs Aware Resource Library .

• Ready to get certified? Learn about training >
• Want to implement ACE screening at your practice? Learn how >
• ACE Screening, Clinical Assessment, and Treatment Planning for Toxic Stress
• Overview: A Tiered Clinical Response Framework for Addressing Toxic Stress
• ACE Screening Clinical Workflows, ACEs and Toxic Stress Risk Assessment Algorithm, and ACE-Associated Health Conditions: For Pediatrics & Adults
• ACEs Aware Self-Care Tool for Pediatrics (To Print)
• ACEs Aware Self-Care Tool for Adults (To Print)

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Adverse Childhood Experiences (ACE) Assessment in Clinical Practice: A Pediatric Integrated Care Model

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Priya Pathak , Katherine Grimes; Adverse Childhood Experiences (ACE) Assessment in Clinical Practice: A Pediatric Integrated Care Model. Pediatrics August 2019; 144 (2_MeetingAbstract): 00. 10.1542/peds.144.2MA1.00d

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Background: The research on long-term physical and mental health effects of adverse childhood experiences (ACE) demonstrates the need to screen for, prevent, and intercept child trauma exposure. However, best practices regarding ACE assessment in pediatric clinical contexts, including integrated models of care, have not been studied. Research shows that mental health services provided within integrated care models can have positive health benefits for children and families facing adversities, however, ACE scores are not generally used to guide these services. Our aim is to gain insight into the challenges and opportunities of using of ACE evaluation to direct clinical and community mental health practice. Methods: This is a qualitative study nested within a SAMHSA-funded integrated care initiative "Enhancing Systems of Care" (ESOC) whereby patients receive enhanced care via integrated teams that include Family Navigators (peer to peer parent support staff), Clinical Care Managers (licensed social workers), and a Child Psychiatry consultant. There are 4 ESOC intervention sites within the Boston metropolitan area. Baseline assessment in the ESOC model includes ACE screening using the CYW Adverse Childhood Experiences Questionnaire (ACE-Q). This qualitatve study is focused on the impact of ACE assessment on clinical care delivery. Unlike research applications that rely only on cumulative ACE score, ESOC assessment uses individual ACE items to further guide therapeutic dialogue, clinical referrals, and recommended interventions. In-depth interviews and focus groups were conducted with clinicians to explore provider perspectives on the value and feasibility of integrating ACE assessment into the clinical environment. Findings: Preliminary data suggests that when incorporated into an integrated care model, ACE assessment is acceptable to both parents and adolescents. Furthermore, the ACE assessment can be effectively streamlined into existing protocols without imposing a burden on providers. Initial qualitative findings from providers suggest that ACE assessment is most useful in the context of therapeutic dialogue, and is clinically relevant in terms of fostering peer to peer relationships, guiding further clinical assessment, and informing recommendations. Conclusions: Incorporation of ACE assessment into an integrated model of child mental health care in is acceptable to families, logistically feasible, and clinically relevant. Implications:: Our findings corroborate prior literature suggesting that the ACE screening process can be therapeutic in fostering dialogue and providing supportive acknowledgement for families. Within this context, further research is needed to assess the impact of ACE assessment and ACEguided intervention on patient clinical functioning, family satisfaction, and child mental health services access and utilization.

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Adverse childhood experience (ace).

Description

Creator and Context

Presenting conditions, administration, desired audience, pratical application, considerations.

How to score

Questionnaire Completion

Interpretation

Clinical considerations, research summary, adverse childhood experience (ace) overview.

The Adverse Childhood Experiences (ACE) assessment is a tool used to identify the presence and impact of negative experiences in childhood. It is a simple scoring method that tallies various types of abuse, neglect, and household challenges faced during childhood. The ACE assessment helps in understanding the cumulative effect of these experiences on a person's health and well-being. The ACE assessment was developed from the Adverse Childhood Experiences Study, a large-scale investigation conducted by the Centers for Disease Control and Prevention and Kaiser Permanente. Dr. Vincent Felitti and Dr. Robert Anda led the study in the 1990s, focusing on the long-term effects of childhood trauma.

The ACE questionnaire measures 10 types of childhood adversity: physical, emotional, and sexual abuse; physical and emotional neglect; and household dysfunction including mental illness, incarcerated relatives, domestic violence, substance abuse, and divorce.

The ACE assessment is typically a self-reported questionnaire consisting of 10 questions, with each affirmative answer scoring one point. This assessment can be administered in a clinical setting or as part of a broader health assessment.

The ACE assessment is suitable for adults, often used retrospectively to understand their childhood experiences. It is also used in research settings to explore the relationship between early adversity and later health outcomes.

Practical Application

Professionals using the ACE assessment should create a supportive environment, as the questionnaire can evoke distressing memories. It's important to ensure that the individual understands the purpose of the assessment and has access to appropriate support services.

While the ACE score provides valuable insights, it should not be used as a diagnostic tool. It is essential to consider the context and individual differences in resilience and coping mechanisms.

How to score the Adverse Childhood Experience (ACE)

Conducting the assessment.

The individual responds to each of the 10 questions with “Yes” or “No”. The assessment is straightforward and can be completed without clinical supervision, but discussion with a healthcare provider is recommended for a comprehensive understanding.

Each affirmative answer in the questionnaire counts as one point. A higher score indicates a higher number of adverse childhood experiences. The ACE score is used to assess the potential risk of developing physical, mental, and social problems. While there is no definitive threshold, a higher ACE score indicates a greater risk of experiencing various health issues. However, it is not deterministic and should be interpreted within the context of individual life circumstances.

Clinicians should use the ACE assessment as part of a broader evaluation. Discussing the results should be done sensitively, with an emphasis on resilience and coping strategies. Referrals to appropriate mental health services may be necessary.

Adverse Childhood Experience (ACE) use cases

The ACE score is beneficial in:

Identifying individuals at risk of health problems due to childhood adversity.

Guiding therapeutic interventions and strategies.

Informing public health policies and preventive measures.

Felitti, V. J., et al. (1998). Relationship of Childhood Abuse and Household Dysfunction to Many of the Leading Causes of Death in Adults. American Journal of Preventive Medicine .

Hughes, K., et al. (2017). The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis. The Lancet Public Health .

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