Objective The purpose of this investigation was to examine treatment adherence to medication and lifestyle recommendations among pediatric migraine patients using electronic monitoring systems. Background Nonadherence to medical treatment is a significant public health concern, and can result in poorer treatment outcomes, decreased cost-effectiveness of medical care, and increased morbidity. No studies have systematically examined adherence to medication and lifestyle recommendations in adolescents with migraine outside of a clinical trial. Methods Participants included 56 adolescents ages 11 – 17 who were presenting for clinical care. All were diagnosed with migraine with or without aura or chronic migraine and had at least 4 headache days per month. Medication adherence was objectively measured using electronic monitoring systems (Medication Event Monitoring Systems technology) and daily, prospective self-report via personal electronic devices. Adherence to lifestyle recommendations of regular exercise, eating, and fluid intake were also assessed using daily self-report on personal electronic devices. Results Electronic monitoring indicates that adolescents adhere to their medication 75% of the time, which was significantly higher than self-reported rates of medication adherence (64%). Use of electronic monitoring of medication detected rates of adherence that were significantly higher for participants taking once daily medication (85%) versus participants taking twice daily medication (59%). Average reported adherence to lifestyle recommendations of consistent non-caffeinated fluid intake (M = 5 cups per day) was below recommended levels of a minimum of 8 cups per day. Participants on average also reported skipping 1 meal per week despite recommendations of consistently eating three meals per day. Conclusions Results suggest that intervention focused on adherence to preventive treatments (such as medication) and lifestyle recommendations may provide more optimal outcomes for children and adolescents with migraine and their families. Once daily dosing of medication may be preferred to twice daily medication for increased medication adherence among children and adolescents.

Objective Substance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data. Materials and Methods Pediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC). Results The dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively). Conclusions It is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies.

Including children in protective custody (e.g., foster care) in legal decisions positively impacts their perceptions of the legal system, with giving youth a voice being particularly important. Studies have primarily focused on including young people in legal processes; however, for adolescents in protective custody, decisions about living arrangements, education, and long-term planning are made outside the courtroom, with ramifications for young people and their perceptions of both legal and child protection systems. This study looks at such decision making using existing data from 151 adolescents who were ages 16–20 and had been in child welfare protective custody for at least 12 months. During in-person interviews we assessed their desired amount of involvement in a recent decision and their perceptions of their actual involvement. Youth named other individuals involved in decision-making. Data were coded and analysed to identify discrepancies in young people’s perceptions of desired and actual levels of involvement. Results indicate that while the majority of adolescents (96%) are participating in decision-making, they generally desire more involvement in decisions made (64%). Only 7% of youth reported that their level of personal involvement and the involvement of others matched what they desired. The most common individuals identified in a decision made were child protection workers, legal professionals, and caregivers or family members. These findings enhance the existing literature by highlighting the unique issues related to giving young people in protective custody a voice, and provide an empirical foundation for guiding policies around who to involve in every-day decisions made for young people preparing for emancipation from protective custody.

Children in foster care in the United States face unique challenges related to access to health and education services. With the COVID-19 pandemic, many of those services were temporarily disrupted, adding burden to an already strained system. This observational study describes the experiences of licensed and kinship caregivers (N = 186) during the peak of COVID-19 stay-at-home orders and as restrictions to services were lifted, to understand the overall impact of COVID-19 on this already vulnerable population. Purposive sampling methods were used, where caregivers known to have received placement of children prior to, during, and following COVID-19 stay-at-home orders were identified and recruited to complete a 45-minute phone-administered survey assessing stress, risks for contracting COVID-19, strain resulting from COVID-19, and access to services for children in foster care in their care across five domains: healthcare, mental health, education, child welfare, and family visitation. Differences by caregiver type (licensed, kinship) and timing in the pandemic were examined. Licensed and kinship caregivers reported similar social and economic impacts of COVID-19, including similar rates of distress for themselves and the youth placed with them. Almost half of caregivers experienced challenges accessing mental health services, with access to services more disrupted during COVID-19 stay-at-home orders. Caregiver reports regarding the social and economic impacts of COVID-19 were similar across the study, suggesting that lessened restrictions have not alleviated strain for this population.

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