Association of initial opioid prescription duration and an opioid refill by pain diagnosis: Evidence from outpatient settings in ten US health systems.

OBJECTIVE: The Centers for Disease Control and Prevention's 2022 Clinical Practice Guideline for Prescribing Opioids for Pain cautioned that inflexible opioid prescription duration limits may harm patients. Information about the relationship between initial opioid prescription duration and a subsequent refill could inform prescribing policies and practices to optimize patient outcomes. We assessed the association between initial opioid duration and an opioid refill prescription. METHODS: We conducted a retrospective cohort study of adults ≥19 years of age in 10 US health systems between 2013 and 2018 from outpatient care with a diagnosis for back pain without radiculopathy, back pain with radiculopathy, neck pain, joint pain, tendonitis/bursitis, mild musculoskeletal pain, severe musculoskeletal pain, urinary calculus, or headache. Generalized additive models were used to estimate the association between opioid days' supply and a refill prescription. RESULTS: Overall, 220,797 patients were prescribed opioid analgesics upon an outpatient visit for pain. Nearly a quarter (23.5%) of the cohort received an opioid refill prescription during follow-up. The likelihood of a refill generally increased with initial duration for most pain diagnoses. About 1 to 3 fewer patients would receive a refill within 3 months for every 100 patients initially prescribed 3 vs. 7 days of opioids for most pain diagnoses. The lowest likelihood of refill was for a 1-day supply for all pain diagnoses, except for severe musculoskeletal pain (9 days' supply) and headache (3-4 days' supply). CONCLUSIONS: Long-term prescription opioid use increased modestly with initial opioid prescription duration for most but not all pain diagnoses examined.

Comparing enhancements to well-child visits in the prevention of obesity: ENCIRCLE cluster-randomized controlled trial.

BACKGROUND: Obesity disproportionally impacts rural, lower-income children in the United States. Primary care providers are well-positioned to engage parents in early obesity prevention, yet there is a lack of evidence regarding the most effective care delivery models. The ENCIRCLE study, a pragmatic cluster-randomized controlled trial, will respond to this gap by testing the comparative effectiveness of standard care well-child visits (WCV) versus two enhancements: adding a patient-reported outcome (PRO) measure (PRO WCV) and PRO WCV plus Food Care (telehealth coaching and a grocery store tour). METHODS: A total of 2,025 parents and their preschool-aged children (20-60 months of age) will be recruited from 24 Geisinger primary care clinics, where providers are randomized to the standard WCV, PRO WCV, or PRO WCV plus Food Care intervention arms. The PRO WCV includes the standard WCV plus collection of the PRO-the Family Nutrition and Physical Activity (FNPA) risk assessment-from parents. Parents complete the PRO in the patient-portal or in the clinic (own device, tablet, or kiosk), receive real-time feedback, and select priority topics to discuss with the provider. These results are integrated into the child's electronic health record to inform personalized preventive counseling by providers. PRO WCV plus Food Care includes referrals to community health professionals who deliver evidence-based obesity prevention and food resource management interventions via telehealth following the WCV. The primary study outcome is change in child body mass index z-score (BMIz), based on the World Health Organization growth standards, 12 months post-baseline WCV. Additional outcomes include percent of children with overweight and obesity, raw BMI, BMI50, BMIz extended, parent involvement in counseling, health behaviors, food resource management, and implementation process measures. DISCUSSION: Study findings will inform health care systems' choices about effective care delivery models to prevent childhood obesity among a high-risk population. Additionally, dissemination will be informed by an evaluation of mediating, moderating, and implementation factors. TRIAL REGISTRATION: ClinicalTrials.gov identifier (NCT04406441); Registered May 28, 2020.

Urban-Rural Differences in Health Care Utilization and COVID-19 Outcomes in Patients With Type 2 Diabetes.

INTRODUCTION: Two studies in Pennsylvania aimed to determine whether community type and community socioeconomic deprivation (CSD) 1) modified associations between type 2 diabetes (hereinafter, diabetes) and COVID-19 hospitalization outcomes, and 2) influenced health care utilization among individuals with diabetes during the COVID-19 pandemic. METHODS: The hospitalization study evaluated a retrospective cohort of patients hospitalized with COVID-19 through 2020 for COVID-19 outcomes: death, intensive care unit (ICU) admission, mechanical ventilation, elevated D-dimer, and elevated troponin level. We used adjusted logistic regression models, adding interaction terms to evaluate effect modification by community type (township, borough, or city census tract) and CSD. The utilization study included patients with diabetes and a clinical encounter between 2017 and 2020. Autoregressive integrated moving average time-series models evaluated changes in weekly rates of emergency department and outpatient visits, hemoglobin A1c (HbA1c) laboratory tests, and antihyperglycemic medication orders from 2018 to 2020. RESULTS: In the hospitalization study, of 2,751 patients hospitalized for COVID-19, 1,020 had diabetes, which was associated with ICU admission and elevated troponin. Associations did not differ by community type or CSD. In the utilization study, among 93,401 patients with diabetes, utilization measures decreased in March 2020. Utilization increased in July, and then began to stabilize or decline through the end of 2020. Changes in HbA1c tests and medication order trends during the pandemic differed by community type and CSD. CONCLUSION: Diabetes was associated with selected outcomes among individuals hospitalized for COVID-19, but these did not differ by community features. Utilization trajectories among individuals with diabetes during the pandemic were influenced by community type and CSD and could be used to identify individuals at risk of gaps in diabetes care.

Risk factors for Lyme disease stage and manifestation using electronic health records.

BACKGROUND: Little is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation. METHODS: We identified Lyme disease cases in 2012-2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status. RESULTS: Among 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease. CONCLUSIONS: The associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

Proximity to freshwater blue space and type 2 diabetes onset: the importance of historical and economic context.

Salutogenic effects of living near aquatic areas (blue space) remain underexplored, particularly in non-coastal and non-urban areas. We evaluated associations of residential proximity to inland freshwater blue space with new onset type 2 diabetes (T2D) in central and northeast Pennsylvania, USA, using medical records to conduct a nested case-control study. T2D cases (n=15,888) were identified from diabetes diagnoses, medication orders, and laboratory test results and frequency-matched on age, sex, and encounter year to diabetes-free controls (n=79,435). We calculated distance from individual residences to the nearest lake, river, tributary, or large stream, and residence within the 100-year floodplain. Logistic regression models adjusted for community socioeconomic deprivation and other confounding variables and stratified by community type (townships [rural/suburban], boroughs [small towns], city census tracts). Compared to individuals living ≥1.25 miles from blue space, those within 0.25 miles had 8% and 17% higher odds of T2D onset in townships and boroughs, respectively. Among city residents, T2D odds were 38-39% higher for those living 0.25 to <0.75 miles from blue space. Residing within the floodplain was associated with 16% and 14% higher T2D odds in townships and boroughs. A post-hoc analysis demonstrated patterns of lower residential property values with nearer distance to the region's predominant waterbody, suggesting unmeasured confounding by socioeconomic disadvantage. This may explain our unexpected findings of higher T2D odds with closer proximity to blue space. Our findings highlight the importance of historic and economic context and interrelated factors such as flood risk and lack of waterfront development in blue space research.

Peridomestic and community-wide landscape risk factors for Lyme disease across a range of community contexts in Pennsylvania.

Land use and forest fragmentation are thought to be major drivers of Lyme disease incidence and its geographic distribution. We examined the association between landscape composition and configuration and Lyme disease in a population-based case control study in the Geisinger health system in Pennsylvania. Lyme disease cases (n = 9657) were identified using a combination of diagnosis codes, laboratory codes, and antibiotic orders from electronic health records (EHRs). Controls (5:1) were randomly selected and frequency matched on year, age, and sex. We measured six landscape variables based on prior literature, derived from the National Land Cover Database and MODIS satellite imagery: greenness (normalized difference vegetation index), percent forest, percent herbaceous, forest edge density, percent forest-herbaceous edge, and mean forest patch size. We assigned landscape variables within two spatial contexts (community and ½-mile [805 m] Euclidian residential buffer). In models stratified by community type, landscape variables were modeled as tertiles and flexible splines and associations were adjusted for demographic and clinical covariates. In general, we observed positive associations between landscape metrics and Lyme disease, except for percent herbaceous, where associations differed by community type. For example, compared to the lowest tertile, individuals with highest tertile of greenness in residential buffers had higher odds of Lyme disease (odds ratio: 95% confidence interval [CI]) in townships (1.73: 1.55, 1.93), boroughs (1.70: 1.40, 2.07), and cities (3.71: 1.74, 7.92). Similarly, corresponding odds ratios (95% CI) for forest edge density were 1.34 (1.22, 1.47), 1.56 (1.33, 1.82), and 1.90 (1.13, 3.18). Associations were generally higher in residential buffers, compared to community, and in cities, compared to boroughs or townships. Our results reinforce the importance of peridomestic landscape in Lyme disease risk, particularly measures that reflect human interaction with tick habitat. Linkage of EHR data to public data on residential and community context may lead to new health system-based approaches for improving Lyme disease diagnosis, treatment, and prevention.

Developing a Framework to Infer Opioid Use Disorder Severity From Clinical Notes to Inform Natural Language Processing Methods: Characterization Study.

BACKGROUND: Information regarding opioid use disorder (OUD) status and severity is important for patient care. Clinical notes provide valuable information for detecting and characterizing problematic opioid use, necessitating development of natural language processing (NLP) tools, which in turn requires reliably labeled OUD-relevant text and understanding of documentation patterns. OBJECTIVE: To inform automated NLP methods, we aimed to develop and evaluate an annotation schema for characterizing OUD and its severity, and to document patterns of OUD-relevant information within clinical notes of heterogeneous patient cohorts. METHODS: We developed an annotation schema to characterize OUD severity based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition. In total, 2 annotators reviewed clinical notes from key encounters of 100 adult patients with varied evidence of OUD, including patients with and those without chronic pain, with and without medication treatment for OUD, and a control group. We completed annotations at the sentence level. We calculated severity scores based on annotation of note text with 18 classes aligned with criteria for OUD severity and determined positive predictive values for OUD severity. RESULTS: The annotation schema contained 27 classes. We annotated 1436 sentences from 82 patients; notes of 18 patients (11 of whom were controls) contained no relevant information. Interannotator agreement was above 70% for 11 of 15 batches of reviewed notes. Severity scores for control group patients were all 0. Among noncontrol patients, the mean severity score was 5.1 (SD 3.2), indicating moderate OUD, and the positive predictive value for detecting moderate or severe OUD was 0.71. Progress notes and notes from emergency department and outpatient settings contained the most and greatest diversity of information. Substance misuse and psychiatric classes were most prevalent and highly correlated across note types with high co-occurrence across patients. CONCLUSIONS: Implementation of the annotation schema demonstrated strong potential for inferring OUD severity based on key information in a small set of clinical notes and highlighting where such information is documented. These advancements will facilitate NLP tool development to improve OUD prevention, diagnosis, and treatment.

Clinical stakeholders' perceptions of patient engagement in outpatient medication treatment for opioid use disorder: A qualitative study.

INTRODUCTION: Medications for opioid use disorder (MOUD) reduce risk of opioid overdose and promote recovery from opioid use disorder, but poor retention in MOUD limits these positive effects. This study explored patient engagement in MOUD from the perspective of clinical stakeholders within an outpatient addiction medicine program to identify program factors influencing patient engagement with treatment. METHODS: We conducted a qualitative case study of a multi-clinic outpatient addiction medicine program embedded within an integrated health system that serves a geographically diverse area of Pennsylvania. Collectively, the program's clinics provide MOUD (primarily buprenorphine) to ~2000 patients annually. From January to March 2021, we conducted semi-structured telephone/video interviews with three stakeholder groups involved in delivering MOUD: administrators (n = 4), providers (n = 7), and addiction care coordinators (n = 5). Data analysis utilized the framework method. RESULTS: We identified five themes related to patient engagement. First, participants described health system integration as enhancing quality and offering opportunities for addressing patients' comprehensive health care needs. However, lack of knowledge about addiction and stigma among health system providers was felt to limit patient benefits from this integration, including access to MOUD. Second, participants viewed patient engagement as central to the program's policies, practices, and clinical environment. Adoption of a harm reduction approach and maintenance of a non-stigmatizing clinic environment were described as essential facilitators of engagement. Third, while clinics followed uniform operations, physician leads expressed differing philosophical approaches to treatment, which participants associated with variations in clinical practice and patient engagement. Fourth, participants identified key services that bolstered engagement in MOUD, including psychosocial services, psychiatric care, and telemedicine. Finally, staff well-being emerged as a key consideration for patient engagement. CONCLUSIONS: Understanding perceptions of those who administer and deliver care is critical for identifying barriers and facilitators to patient engagement in MOUD. Findings suggest potential opportunities for addiction treatment programs to improve patient engagement and ultimately MOUD retention, including integration with other healthcare services to meet comprehensive healthcare needs; adoption of a harm reduction approach; creation of non-stigmatizing clinical environments; investment in psychosocial services, psychiatric care, and telemedicine; and prioritization of staff wellness.

Families matter! Presexual risk prevention intervention.

Parent-based HIV prevention programming may play an important role in reaching youths early to help establish lifelong patterns of safe and healthy sexual behaviors. Families Matter! is a 5-session, evidence-based behavioral intervention designed for primary caregivers of children aged 9 to 12 years to promote positive parenting and effective parent-child communication about sexuality and sexual risk reduction. The program's 5-step capacity-building model was implemented with local government, community, and faith-based partners in 8 sub-Saharan African countries with good intervention fidelity and high levels of participant retention. Families Matter! may be useful in other resource-constrained settings.

Localization of health systems in low- and middle-income countries in response to long-term increases in energy prices.

External challenges to health systems, such as those caused by global economic, social and environmental changes, have received little attention in recent debates on health systems' performance in low-and middle-income countries (LMICs). One such challenge in coming years will be increasing prices for petroleum-based products as production from conventional petroleum reserves peaks and demand steadily increases in rapidly-growing LMICs. Health systems are significant consumers of fossil fuels in the form of petroleum-based medical supplies; transportation of goods, personnel and patients; and fuel for lighting, heating, cooling and medical equipment. Long-term increases in petroleum prices in the global market will have potentially devastating effects on health sectors in LMICs who already struggle to deliver services to remote parts of their catchment areas. We propose the concept of "localization," originating in the environmental sustainability literature, as one element of response to these challenges. Localization assigns people at the local level a greater role in the production of goods and services, thereby decreasing reliance on fossil fuels and other external inputs. Effective localization will require changes to governance structures within the health sector in LMICs, empowering local communities to participate in their own health in ways that have remained elusive since this goal was first put forth in the Alma-Ata Declaration on Primary Health Care in 1978. Experiences with decentralization policies in the decades following Alma-Ata offer lessons on defining roles and responsibilities, building capacity at the local level, and designing appropriate policies to target inequities, all of which can guide health systems to adapt to a changing environmental and energy landscape.

Implementation of Telemedicine Delivery of Medications for Opioid Use Disorder in Pennsylvania Treatment Programs During COVID-19.

INTRODUCTION: Temporary policy changes during the coronavirus disease 2019 pandemic facilitated rapid expansion of medication for opioid use disorder via telemedicine (tele-MOUD). Evidence for tele-MOUD best practices and its impact on treatment engagement and retention remains limited. This quality improvement initiative compared tele-MOUD implementation among Pennsylvania medication for opioid use disorder (MOUD) programs, evaluated sociodemographic characteristics of patients using tele-MOUD, and described trends in tele-MOUD use and patient engagement and retention. METHODS: Five health systems with MOUD programs completed questionnaires regarding their tele-MOUD models and provided aggregated sociodemographic data for MOUD patients with in-person and telemedicine visits in 2020. Three programs provided aggregated monthly appointment data (scheduled, completed, no-show, tele-MOUD visits) over the period in which tele-MOUD scaled up. RESULTS: Differences in tele-MOUD protocols related to provision of tele-MOUD inductions, patient eligibility for tele-MOUD, and operationalization of remote drug testing. Across programs, 88% of prescribers conducted tele-MOUD appointments, and 50% of patients used tele-MOUD in 2020. We observed sociodemographic differences, with a greater proportion of female, White, and non-Hispanic patients using tele-MOUD. Across programs with appointment data, overall patient enrollment increased, and new patient enrollment remained relatively constant. Engagement trends suggested a temporary decline in no-show appointments that aligned with the escalation of tele-MOUD in one program. CONCLUSIONS: Tele-MOUD protocol differences indicate a need for research to inform evidence-based guidance. Findings suggest that patients largely remained engaged and retained in MOUD as tele-MOUD was implemented but reveal inequities in tele-MOUD use, highlighting the need for efforts to overcome technology access barriers and avoid exacerbating disparities in MOUD access.

Identification of opioid use disorder using electronic health records: Beyond diagnostic codes.

BACKGROUND: We used structured and unstructured electronic health record (EHR) data to develop and validate an approach to identify moderate/severe opioid use disorder (OUD) that includes individuals without prescription opioid use or chronic pain, an underrepresented population. METHODS: Using electronic diagnosis grouper text from EHRs of ~1 million patients (2012-2020), we created indicators of OUD-with "tiers" indicating OUD likelihood-combined with OUD medication (MOUD) orders. We developed six sub-algorithms with varying criteria (multiple vs single MOUD orders, multiple vs single tier 1 indicators, tier 2 indicators, tier 3 and 4 indicators). Positive predictive values (PPVs) were calculated based on chart review to determine OUD status and severity. We compared demographic and clinical characteristics of cases identified by the sub-algorithms. RESULTS: In total, 14,852 patients met criteria for one of the sub-algorithms. Five sub-algorithms had PPVs ≥0.90 for any severity OUD; four had PPVs ≥0.90 for moderate/severe OUD. Demographic and clinical characteristics differed substantially between groups. Of identified OUD cases, 31.3% had no past opioid analgesic orders, 79.7% lacked evidence of chronic prescription opioid use, and 43.5% lacked a chronic pain diagnosis. DISCUSSION: Incorporating unstructured data with MOUD orders yielded an approach that adequately identified moderate/severe OUD, identified unique demographic and clinical sub-groups, and included individuals without prescription opioid use or chronic pain, whose OUD may stem from illicit opioids. Findings show that incorporating unstructured data strengthens EHR algorithms for identifying OUD and suggests approaches limited to populations with prescription opioid use or chronic pain exclude many individuals with OUD.

Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples.

INTRODUCTION: Inequitable access to leisure-time physical activity (LTPA) resources may explain geographic disparities in type 2 diabetes (T2D). We evaluated whether the neighborhood socioeconomic environment (NSEE) affects T2D through the LTPA environment. RESEARCH DESIGN AND METHODS: We conducted analyses in three study samples: the national Veterans Administration Diabetes Risk (VADR) cohort comprising electronic health records (EHR) of 4.1 million T2D-free veterans, the national prospective cohort REasons for Geographic and Racial Differences in Stroke (REGARDS) (11 208 T2D free), and a case-control study of Geisinger EHR in Pennsylvania (15 888 T2D cases). New-onset T2D was defined using diagnoses, laboratory and medication data. We harmonized neighborhood-level variables, including exposure, confounders, and effect modifiers. We measured NSEE with a summary index of six census tract indicators. The LTPA environment was measured by physical activity (PA) facility (gyms and other commercial facilities) density within street network buffers and population-weighted distance to parks. We estimated natural direct and indirect effects for each mediator stratified by community type. RESULTS: The magnitudes of the indirect effects were generally small, and the direction of the indirect effects differed by community type and study sample. The most consistent findings were for mediation via PA facility density in rural communities, where we observed positive indirect effects (differences in T2D incidence rates (95% CI) comparing the highest versus lowest quartiles of NSEE, multiplied by 100) of 1.53 (0.25, 3.05) in REGARDS and 0.0066 (0.0038, 0.0099) in VADR. No mediation was evident in Geisinger. CONCLUSIONS: PA facility density and distance to parks did not substantially mediate the relation between NSEE and T2D. Our heterogeneous results suggest that approaches to reduce T2D through changes to the LTPA environment require local tailoring.

Classifying Characteristics of Opioid Use Disorder From Hospital Discharge Summaries Using Natural Language Processing.

Background: Opioid use disorder (OUD) is underdiagnosed in health system settings, limiting research on OUD using electronic health records (EHRs). Medical encounter notes can enrich structured EHR data with documented signs and symptoms of OUD and social risks and behaviors. To capture this information at scale, natural language processing (NLP) tools must be developed and evaluated. We developed and applied an annotation schema to deeply characterize OUD and related clinical, behavioral, and environmental factors, and automated the annotation schema using machine learning and deep learning-based approaches. Methods: Using the MIMIC-III Critical Care Database, we queried hospital discharge summaries of patients with International Classification of Diseases (ICD-9) OUD diagnostic codes. We developed an annotation schema to characterize problematic opioid use, identify individuals with potential OUD, and provide psychosocial context. Two annotators reviewed discharge summaries from 100 patients. We randomly sampled patients with their associated annotated sentences and divided them into training (66 patients; 2,127 annotated sentences) and testing (29 patients; 1,149 annotated sentences) sets. We used the training set to generate features, employing three NLP algorithms/knowledge sources. We trained and tested prediction models for classification with a traditional machine learner (logistic regression) and deep learning approach (Autogluon based on ELECTRA's replaced token detection model). We applied a five-fold cross-validation approach to reduce bias in performance estimates. Results: The resulting annotation schema contained 32 classes. We achieved moderate inter-annotator agreement, with F1-scores across all classes increasing from 48 to 66%. Five classes had a sufficient number of annotations for automation; of these, we observed consistently high performance (F1-scores) across training and testing sets for drug screening (training: 91-96; testing: 91-94) and opioid type (training: 86-96; testing: 86-99). Performance dropped from training and to testing sets for other drug use (training: 52-65; testing: 40-48), pain management (training: 72-78; testing: 61-78) and psychiatric (training: 73-80; testing: 72). Autogluon achieved the highest performance. Conclusion: This pilot study demonstrated that rich information regarding problematic opioid use can be manually identified by annotators. However, more training samples and features would improve our ability to reliably identify less common classes from clinical text, including text from outpatient settings.

Application of the COM-B model to patient barriers and facilitators of retention in medication treatment for opioid use disorder in rural Northeastern United States: A qualitative study.

Introduction: Medications for opioid use disorder (MOUD) reduce illicit opioid use and overdose mortality, but effectiveness remains limited by poor treatment retention. Understanding multilevel barriers and facilitators to retention from the patient perspective can guide intervention strategies to improve retention. Methods: We conducted semi-structured telephone interviews to elicit perspectives of individuals with opioid use disorder (OUD) currently (n = 19) and formerly (n = 16) receiving treatment from a multi-clinic outpatient MOUD program in Pennsylvania in July 2020 to January 2021. The Capability, Opportunity, Motivation, Behavior model provided a theoretical framework for analysis. Results: Based on interview themes, physical, rather than psychological, capability was more salient to MOUD engagement, and pertained to individual-level factors such as side effects, withdrawal, and the degree to which MOUD addressed participants' need for pain management. Co-existing mental health conditions also challenged participants' physical ability to attend appointments. The opportunity domain contained both physical and social aspects. Physical opportunity for MOUD engagement centered on community-level factors related to MOUD access (e.g., distance, transportation) and clinical-level factors including program policies. Themes related to social opportunity included interpersonal influences-such as therapeutic and social support-and stigma associated with OUD and MOUD. Motivation emerged as the dominant domain for patients. Reflective motivation factors included individual-level factors such as participants' recognition of their addiction and "readiness" to quit illicit opioid use, attitudes toward MOUD, future treatment expectations, motivation to engage in MOUD, and perceived consequences of disengagement. Automatic motivation factors included the degree to which MOUD created a sense of normalcy for participants and the use of illicit drugs to numb emotions. Conclusions: Factors at the individual, interpersonal, clinical, community, and societal levels influenced patients' capability, opportunity, and motivation to engage in MOUD. Understanding such factors can inform implementation strategies to improve retention.

Family-Based Telehealth Initiative to Improve Nutrition and Physical Activity for Children With Obesity and Its Utility During COVID-19: A Mixed Methods Evaluation.

Guidelines recommend primary care providers refer children with obesity to behavioral interventions, but given limited program availability, access, and parental engagement, referrals remain rare. We developed telehealth coaching interventions for families whose children received care at a health system in Pennsylvania, United States in 2019-2020. Intervention referrals were facilitated by the pediatrician and/or project team for 6-12-year-old children with obesity following well-child visits. Participants chose one of three 26-week interventions focused on healthy eating, physical activity, or a hybrid clinical/nutrition intervention. Interventions engaged parents as change agents, enhancing self-efficacy to model and reinforce behavior and providing resources to help create a healthy home environment. We enrolled 77 of 183 eligible parent/child dyads. We used mixed methods to evaluate the interventions. Repeated measures models among participants showed significant reductions in obesogenic nutrition behaviors post-intervention and at 1-year follow-up, including a reduction in sugar-sweetened beverage intake of 2.14 servings/week (95% confidence interval: -3.45, -0.82). There were also improvements in obesoprotective nutrition behaviors (e.g., frequency of family meals, parental self-efficacy related to meal management). One year post-baseline, we observed no significant differences in changes in body mass index (BMI) z-scores comparing child participants with matched controls. Given potential impacts of COVID-19 community restrictions on study outcomes, we conducted qualitative interviews with 13 participants during restrictions, which exemplified how disrupted routines constrained children's healthy behaviors but that intervention participation prepared parents by providing cooking and physical activities at home. Findings support the potential of a telehealth-delivered nutrition intervention to support adoption of healthy weight behaviors.

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008-2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients' residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region's census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like-township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Neighborhood Socioeconomic Environment and Risk of Type 2 Diabetes: Associations and Mediation Through Food Environment Pathways in Three Independent Study Samples.

OBJECTIVE: We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural. RESULTS: Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways. CONCLUSIONS: Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.

Introduction: Opioid use disorders (OUDs) constitute a major public health issue, and we urgently need alternative methods for characterizing risk for OUD. Electronic health records (EHRs) are useful tools for understanding complex medical phenotypes but have been underutilized for OUD because of challenges related to underdiagnosis, binary diagnostic frameworks, and minimally characterized reference groups. As a first step in addressing these challenges, a new paradigm is warranted that characterizes risk for opioid prescription misuse on a continuous scale of severity, i.e., as a continuum. Methods: Across sites within the PsycheMERGE network, we extracted prescription opioid data and diagnoses that co-occur with OUD (including psychiatric and substance use disorders, pain-related diagnoses, HIV, and hepatitis C) for over 2.6 million patients across three health registries (Vanderbilt University Medical Center, Mass General Brigham, Geisinger) between 2005 and 2018. We defined three groups based on levels of opioid exposure: no prescriptions, minimal exposure, and chronic exposure and then compared the comorbidity profiles of these groups to the full registries and to those with OUD diagnostic codes. Results: Our results confirm that EHR data reflects known higher prevalence of substance use disorders, psychiatric disorders, medical, and pain diagnoses in patients with OUD diagnoses and chronic opioid use. Comorbidity profiles that distinguish opioid exposure are strikingly consistent across large health systems, indicating the phenotypes described in this new quantitative framework are robust to health systems differences. Conclusion: This work indicates that EHR prescription opioid data can serve as a platform to characterize complex risk markers for OUD using existing data.

Association of Greenness with Blood Pressure among Individuals with Type 2 Diabetes across Rural to Urban Community Types in Pennsylvania, USA.

Greenness may impact blood pressure (BP), though evidence is limited among individuals with type 2 diabetes (T2D), for whom BP management is critical. We evaluated associations of residential greenness with BP among individuals with T2D in geographically diverse communities in Pennsylvania. To address variation in greenness type, we evaluated modification of associations by percent forest. We obtained systolic (SBP) and diastolic (DBP) BP measurements from medical records of 9593 individuals following diabetes diagnosis. Proximate greenness was estimated within 1250-m buffers surrounding individuals' residences using the normalized difference vegetation index (NDVI) prior to blood pressure measurement. Percent forest was calculated using the U.S. National Land Cover Database. Linear mixed models with robust standard errors accounted for spatial clustering; models were stratified by community type (townships/boroughs/cities). In townships, the greenest communities, an interquartile range increase in NDVI was associated with reductions in SBP of 0.87 mmHg (95% CI: -1.43, -0.30) and in DBP of 0.41 mmHg (95% CI: -0.78, -0.05). No significant associations were observed in boroughs or cities. Evidence for modification by percent forest was weak. Findings suggest a threshold effect whereby high greenness may be necessary to influence BP in this population and support a slight beneficial impact of greenness on cardiovascular disease risk.