Sonya Negriff’s research while affiliated with Kaiser Permanente and other places

Autistic youth often experience co-occurring mental health needs, yet they have multi-level barriers to accessing needed care. To address these barriers, the ATTAIN NAV (Access to Tailored Autism Integrated Care through Family Navigation) intervention was co-designed with caregiver and healthcare partners and delivered by lay health navigators to facilitate access to and engagement with mental health services for school-age autistic youth. This manuscript describes the multi-method, partner-engaged, longitudinal adaptation process to (1) identify intervention content and implementation refinements prior to the hybrid trial and (2) track ongoing research, intervention, and implementation adaptations during the trial and their impacts on study outcomes. The adaptation processes used the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies (Miller et al., 2021) to guide data collection and evaluation approaches. From the qualitative co-design activities with caregivers (n = 5), primary care providers (n = 6), developmental care clinicians (n = 4), and health informatics staff (n = 3), several intervention content and implementation adaptations were identified and integrated prior to the trial. From the longitudinal adaptation tracking process during the trial, a total of 19 adaptations were documented throughout the implementation trial. The adaptations were related to maintaining the feasibility and acceptability of the study procedures (32%), increasing family recruitment/engagement (26%), increasing the acceptability of the intervention components (16%), increasing physician recruitment/engagement (11%), expanding mental health resources (5%), complying with partnered healthcare organization policy (5%), and increasing navigator workflow efficiency (5%). Findings offer a structured and replicable approach adoptable by non-traditional mental health intervention and implementation research.

ATTAIN NAV (Access to Tailored Autism Integrated Care through Family Navigation) was delivered by family navigators to promote access to and engagement with mental health services for school-age autistic youth. This study used a mixed method, stepped wedge design to test the effects of family navigation on service and clinical outcomes while gathering information on implementation. Primary care providers from six clinics in California and 56 caregiver-child dyads enrolled in and completed the study. Clinics were randomized to either a technology-enhanced or standard family navigation condition. Caregivers completed assessments at baseline and post about child, family and services outcomes, and a subset participated in a post qualitative interview. Quantitative findings demonstrated improvements in child challenging behavior and parent activation across conditions although these improvements were more pronounced for families in the standard FN condition. At post-intervention, families in the standard FN condition reported higher levels of navigation satisfaction, a shorter time to attend their first mental health appointment, and higher engagement with their navigator. Qualitative findings complemented and expanded the quantitative survey findings. The ATTAIN NAV model of family navigation for autistic children with co-occurring mental health needs demonstrates promising implementation, service, and clinical benefits. Clinical Trials Registration. NCT05344378.

Background There is increasing interest in screening for adverse childhood experiences in pediatric primary care, but no evidence of the actual consequences on behavioral/mental health services. This study tested the association between initiation of ACEs screening in pediatric primary care and changes in the rate of referrals to social work and visits to social work and behavioral health. Methods Data came from the electronic health records of children and adolescents between 2 and 18 years old who were members of a large integrated healthcare system serving Southern California (N = 513,812). Poisson regression was used to compare the rate of referrals and visits to social work and behavioral health visits for clinics doing standardized ACEs screening (i.e., intervention clinics; n = 28) versus clinics not screening (i.e., control clinics; n = 64) during June 1-December 31 2022 as well as for these same months in 2020 and 2021. Results Intervention clinics had an average screening rate of 57% (range 26.8 to 91.9%) and an average positive screen rate of 11% (range 1.6–25.1%). The difference in the adjusted rate from 2021 to 2022 was significantly different between intervention and control clinics for referrals to social work (RR 1.48, 95% CI 1.25, 1.74), but was not statistically different for visits to social work or behavioral health. Conclusions The findings suggest that ACEs screening does not significantly increase the rates of social work and behavioral health visits, although it did increase referrals to social work. We acknowledge that this may vary based on geographic areas and populations served by different healthcare systems.

Importance Data from surveys show increased mental health disorders in youths. However, little is known about clinical diagnosis over time. Objective To assess the incidence, prevalence, and changes from 2017 to 2021 for depression and anxiety diagnosed clinically among children, adolescents, and young adults and to identify potential disparities. Design, Setting, and Participants This cohort study included approximately 1.7 million individuals aged 5 to 22 years in Southern California. Data were extracted from electronic medical records; International Statistical Classification of Diseases, Tenth Revision ( ICD-10 ), codes were used to identify depression and/or anxiety diagnosis for each study year from January 1, 2017, to December 31, 2021. Rates were stratified by age, gender, race and ethnicity, estimated household income, weight status, and comorbidity history. Changes over time and association with these variables were assessed using Poisson regression. Data were analyzed between June 1, 2022, and November 29, 2023. Main Outcomes and Measures Clinical diagnosis of (1) depression and (2) anxiety without a depression diagnosis using ICD-10 codes. Results Among the 1.7 million participants, mean (SD) age was approximately 14 (5) years, and 51% were male. In terms of race and ethnicity for each study year, approximately 50% of participants were Hispanic; 8%, non-Hispanic Asian; 8%, non-Hispanic Black; and 23%, non-Hispanic White. From 2017 to 2021, depression diagnosis increased by 55.6% (from 1.35% to 2.10%) for incidence and 60.0% (from 2.55% to 4.08%) for prevalence; anxiety without depression diagnosis increased by 31.1% (from 1.77% to 2.32%) for incidence and 35.2% (from 3.13% to 4.22%) for prevalence ( P < .001 for trend). The increases in rates were higher during the COVID-19 pandemic (2020-2021) than before the pandemic (2017-2019), except for depression incidence. Rates increased across all subgroups. Rates were highest for subgroups aged 14 to 17 and 18 to 22 years; female participants; those of non-Hispanic American Indian or Alaska Native, non-Hispanic White, or multiple races or ethnicities; and subgroups with higher household income, obesity (and underweight for anxiety without depression), or comorbidities. Among these factors, age was the most important factor for depression diagnosis, whereas weight status was the most important factor for anxiety without depression diagnosis. Conclusions and Relevance This cohort study, using electronic medical record data from a large integrated health care system, found an increase in clinically diagnosed depression from 2017 to 2021, with a higher increase during the COVID-19 pandemic and higher rates in some subgroups. Equally important, this study identified high rates and an increase in clinical diagnosis of anxiety without a depression diagnosis. These results support the increased need in public health and health care effort to combat the mental health crisis in youths.

Objective: This study investigated ICD-10-CM codes for adverse social determinants of health (SDoH) across 12 U.S. health systems by using data from multiple health care encounter types for diverse patients covered by multiple payers. Methods: The authors described documentation of 11 SDoH ICD-10-CM code categories (e.g., educational problems or social environmental problems) between 2016 and 2021; assessed changes over time by using chi-square tests for trend in proportions; compared documentation in 2021 by gender, age, race-ethnicity, and site with chi-square tests; and compared all patients' mental health outcomes in 2021 with those of patients with documented SDoH ICD-10-CM codes by using exact binomial tests and one-proportion z tests. Results: Documentation of any SDoH ICD-10-CM code significantly increased, from 1.7% of patients in 2016 to 2.7% in 2021, as did that for all SDoH categories except educational problems. Documentation was often more prevalent among female patients and those of other or unknown gender than among male patients and among American Indian or Alaska Native, Black or African American, and Hispanic individuals than among those belonging to other race-ethnicity categories. More educational problems were documented for younger patients, and more social environmental problems were documented for older patients. Psychiatric diagnoses and emergency department visits and hospitalizations related to mental health were more common among patients with documented SDoH codes. Conclusions: SDoH ICD-10-CM code documentation was infrequent and differed by population subgroup. Differences may reflect documentation practices or true SDoH prevalence variation. Standardized SDoH documentation methods are needed in health care settings.

Importance Firearm injuries constitute a public health crisis. At the healthcare encounter level, they are, however, rare events. Objective To develop a predictive model to identify healthcare encounters of adult patients at increased risk of firearm injury to target screening and prevention efforts. Materials and Methods Electronic health records data from Kaiser Permanente Southern California (KPSC) were used to identify healthcare encounters of patients with fatal and non-fatal firearm injuries, as well as healthcare visits of a sample of matched controls during 2010-2018. More than 170 predictors, including diagnoses, healthcare utilization, and neighborhood characteristics were identified. Extreme gradient boosting (XGBoost) and a split sample design were used to train and test a model that predicted risk of firearm injury within the next 3 years at the encounter level. Results A total of 3879 firearm injuries were identified among 5 288 529 KPSC adult members. Prevalence at the healthcare encounter level was 0.01%. The 15 most important predictors included demographics, healthcare utilization, and neighborhood-level socio-economic factors. The sensitivity and specificity of the final model were 0.83 and 0.56, respectively. A very high-risk group (top 1% of predicted risk) yielded a positive predictive value of 0.14% and sensitivity of 13%. This high-risk group potentially reduces screening burden by a factor of 11.7, compared to universal screening. Results for alternative probability cutoffs are presented. Discussion Our model can support more targeted screening in healthcare settings, resulting in improved efficiency of firearm injury risk assessment and prevention efforts.

Introduction Information about causes of injury is key for injury prevention efforts. Historically, cause-of-injury coding in clinical practice has been incomplete due to the need for extra diagnosis codes in the International Classification of Diseases-Ninth Revision-Clinical Modification (ICD-9-CM) coding. The transition to ICD-10-CM and increased use of clinical support software for diagnosis coding is expected to improve completeness of cause-of-injury coding. This paper assesses the recording of external cause-of-injury codes specifically for those diagnoses where an additional code is still required. Methods We used electronic health record and claims data from 10 health systems from October 2015 to December 2021 to identify all inpatient and emergency encounters with a primary diagnosis of injury. The proportion of encounters that also included a valid external cause-of-injury code is presented. Results Most health systems had high rates of cause-of-injury coding: over 85% in emergency departments and over 75% in inpatient encounters with primary injury diagnoses. However, several sites had lower rates in both settings. State mandates were associated with consistently high external cause recording. Conclusions Completeness of cause-of-injury coding improved since the adoption of ICD-10-CM coding and increased slightly over the study period at most sites. However, significant variation remained, and completeness of cause-of-injury coding in any diagnosis data used for injury prevention planning should be empirically determined.

Despite well-documented associations between adverse childhood experiences (ACEs) and lifelong impairments in health and well-being, few studies have examined how to facilitate implementation of ACEs screening and referral programs in pediatric settings. We sought to identify facilitators and barriers related to screening for and addressing ACEs in a large integrated healthcare delivery system in Southern California. Using a developmental evaluation approach, we conducted twenty semi-structured interviews with pediatricians, nurses, social workers, and community referral organization staff. Interviews took place across six pediatric clinic pilot sites in Kaiser Permanente Southern California, where more than 7,000 pediatric patients were screened for ACEs between July 2018 and December 2019. Thematic analysis was conducted to identify themes. Key facilitators for screening and referrals for pediatric ACEs screening included providing clinician education to normalize conversations about ACEs, using screening data to provide more holistic and compassionate care, and collaborating across different types of clinicians. Key barriers included screening tool challenges related to patient confusion and cultural differences, capacity limitations, training issues, and care team silos. When used in the context of a trauma- and resilience-informed workforce, ACEs screening may be a powerful tool to support more collaborative and impactful care decisions that move away from symptom management to address root causes and promote prevention.