Background:Electronic health records provide opportunities for improving Lyme disease surveillance by collecting detailed clinical information and timely, scalable disease incidence estimation. Medical record review can improve understanding of clinical manifestations, how likely and possible cases compare, and potential misclassification of possible cases. Methods:The SubLyme Network is a collaboration among 5 healthcare systems, a coordinating center, and the Centers for Disease Control and Prevention. The sites each conducted stratified random sampling of 500 potential acute Lyme disease episodes for medical record review from patients with a diagnosis, test order, and/or appropriate antibiotic in 2022 or 2023. Reviewers abstracted data, including from free text notes, to classify patients as likely or possible Lyme disease and by clinical manifestation. Multivariable logistic regression was used to identify differences between possible compared to likely Lyme disease cases. Results:Among 2500 reviews, 880 patients were categorized as having likely or possible new onset, active Lyme disease. The patient proportion identified as having a specific clinical manifestation ranged from 61% to 76% across sites. Of likely and possible cases, 55.9%, 7.8%, 1.3%, and 3.1% were diagnosed with erythema migrans, Lyme arthritis, Lyme carditis, and neuroborreliosis, respectively. Possible cases differed from likely cases in having more nonspecific symptoms, non-Lyme disease diagnoses, provider uncertainty, and undocumented provider decision-making. Conclusions:Medical record review is expected to be more accurate than computable phenotypes for Lyme disease identification and staging, yet likely and possible cases were challenging to distinguish, and a sizable proportion could not have clinical manifestation assigned. Findings provide direction for computable phenotype refinement to improve case identification. KEY WORDS: clinical manifestations, electronic health records, epidemiology, Lyme disease, surveillance.

Background Lyme disease is the most common vector-borne illness in the United States. The limitations of traditional surveillance strategies for Lyme disease affect the ability to reliably track its burden and evaluate interventions. The US Centers for Disease Control and Prevention (CDC) established the Surveillance Based Lyme Disease Network (SubLyme) in September 2023 to strengthen Lyme disease surveillance and research using electronic health record (EHR) data. Objective SubLyme has three primary objectives: (1) to establish and evaluate criteria for identifying Lyme disease cases in EHR data (ie, create computable phenotypes [CPs]) that can be scaled across diverse health systems, (2) to estimate Lyme disease incidence, and (3) to describe Lyme disease incidence by key demographics. Secondary objectives are to develop CPs that distinguish between acute and disseminated Lyme disease, identify clinical manifestations, and support future research efforts. This paper describes SubLyme, its structure, and its methods. Methods SubLyme includes 5 health systems in 3 US regions with a high risk of Lyme disease: Geisinger, in Pennsylvania; Marshfield Clinic Health System, in Wisconsin; and Mass General Brigham, Tufts Medical Center, and MaineHealth in New England. The network is administered by a coordinating center (Westat) and the US CDC. SubLyme is evaluating the validity of EHR-based CP definitions for Lyme disease. CP performance is assessed by measuring sensitivity, specificity, positive predictive value, and negative predictive value against manually abstracted medical charts. Each site identified a cohort of patients with any Lyme disease element in their EHR (Lyme disease diagnosis code, Lyme disease laboratory test order, and Lyme-appropriate antibiotic order) during 2022 to 2023 and selected 500 charts for manual review as the gold standard against which CP performance was evaluated. SubLyme will use the Lyme disease CPs to generate incidence rates for Lyme disease overall and for various subgroups. Results SubLyme identified 332,256 patients with at least 1 Lyme disease element in their record from more than 4.6 million patients. Of these patients, 55.6% (n=184,734) were female, 87.9% (n=292,053) were White, and 90.8% (n=301,688) were non-Hispanic. More than half of the patients only had a Lyme-appropriate medication order (n=177,425, 53.4%) and 35.8% (n=118,948) only had a Lyme disease test order. The most common combination was a medication order with a laboratory test order (n=22,926, 6.9%), followed by a combination of a diagnosis, test, and medication order (n=5316, 1.6%). Conclusions SubLyme is well positioned to advance Lyme disease surveillance using EHR data across multiple health systems. The exploration of new surveillance methods in Lyme disease is critical as disease frequency increases and the geography expands. An EHR-based approach to surveillance has the potential to overcome challenges of current surveillance strategies and to accelerate Lyme disease research. International Registered Report Identifier (IRRID) DERR1-10.2196/94921

Objectives: Early discontinuation of medication for opioid use disorder (MOUD) is associated with increased mortality and hinders remission. Combining data from electronic health records (EHRs) and patient questionnaires, we examined buprenorphine discontinuation and barriers and facilitators to retention in outpatient settings. Methods: Adults initiating buprenorphine in a Pennsylvania outpatient program (2021–2023) completed 2 questionnaires: at medication initiation (baseline) and 6 months after initiation (follow-up). Among 1189 eligible patients, we evaluated buprenorphine discontinuation (≥30-d gap in medication supply) and compared demographic factors using EHRs. We identified discontinuation reasons among survey participants (baseline n = 374; follow-up n = 197) through medical record review; questionnaires assessed treatment barriers and facilitators. Results: Within 6 months, 629 (53%) patients discontinued buprenorphine, though 15% restarted medication within the 6-month period. Discontinuation was more common among younger patients. Among baseline survey participants, discontinuation was usually unplanned (66%) or against provider advice (19%). Among follow-up participants who discontinued the program during the follow-up period, 54% reported receiving MOUD at 6 months, indicating that many transition to other treatment programs. Those not receiving medication at 6 months cited lack of need (46%). Travel challenges (distance/transportation to clinics), scheduling, and mental health conditions were common barriers to treatment; facilitators centered on improving these factors and clinic processes. Conclusions: Observed patterns reflect dynamic and noncontinuous receipt of medication over time and underscore the early months of treatment as a critical window for supporting buprenorphine engagement. Findings reveal actionable areas to improve retention by reducing logistical burdens, enhancing nonjudgmental therapeutic support, and integrating mental health care.

To elucidate the role of community socioeconomic conditions in creating opioid-related risk environments, we assessed community-level socioeconomic measures in association with opioid use disorder (OUD) across a diverse geography. We conducted a case-control study using medical records (2012–2020) from a Pennsylvania health system to identify cases of OUD (n = 14,674) and controls (n = 58,696; frequency-matched on age, sex, year, medical record duration). Residential addresses were used to assign community-level measures: community socioeconomic deprivation (CSD), high proportional housing costs (HPHC), population in service occupations (PSO), and community credit score (CCS). Logistic regression analyzed associations of community type (city census tracts [CCT], boroughs, townships) and community socioeconomic features (stratified by community type) with OUD, adjusting for demographics and individual-level socioeconomic status. CCT or borough (versus township) residence was associated with higher OUD odds. CSD, HPHC, and CCS were associated with OUD across community types; PSO was only associated in CCTs. The highest (versus lowest) level of CSD was associated (odds ratio, 95

Introduction:Medications for opioid use disorder (MOUD) are standard of care for opioid use disorder (OUD), but high rates of treatment discontinuation limit their impact on recovery. Nature exposure and engagement holds promise as a potential adjunctive treatment to MOUD through stress reduction and mental health benefits. This study evaluated whether nature exposure influenced MOUD treatment participation by analyzing associations of residential greenness with MOUD discontinuation across a diverse geography in Pennsylvania, while considering interrelated factors-season and community type. Methods:We analyzed electronic health records from 2,570 adults receiving MOUD from an outpatient addiction treatment program. Weekly MOUD participation was derived from medication days' supply of buprenorphine or naltrexone. Average weekly greenness (normalized difference vegetation index) was assigned to buffers surrounding participants' residential address. We applied mixed-effects logistic regression of pooled person-weeks in treatment to model the odds of MOUD discontinuation, clustered by patient and with robust standard errors. Results:In models adjusted for sociodemographic factors, residential greenness was not associated with MOUD discontinuation. We observed associations of season with MOUD discontinuation: compared to spring weeks, the odds of MOUD discontinuation were 20-27% higher during summer, fall, and winter weeks. In season-stratified models, we observed a non-linear association of greenness and MOUD discontinuation during the spring season. Conclusions:Understanding factors contributing to MOUD discontinuation is essential to improving recovery outcomes for those with OUD. Findings suggest that passive greenness exposure may have little influence on MOUD participation but identified the potential importance of season on MOUD outcomes.

INTRODUCTION:Prescription opioid dose reductions can raise the risk of adverse events for patients on long-term opioid therapy for noncancer pain. Evidence on whether risks differ by age or sex is needed to support tailored clinical decision-making. METHODS:In 2024, a secondary analysis of an observational cohort study was conducted across 8 U.S. healthcare systems analyzing electronic health records and claims data from a prescription opioid registry (excluding buprenorphine prescriptions) between January 1, 2012, and December 31, 2018, including adults with stable prescription opioid use and a subsequent ≥2-month dose reduction period (n=60,040), yielding 600,234 dose reduction periods as the analytic sample. Differences in the association between dose reduction level (1% to <15%, 15% to <30%, 30% to <100%, and 100% from baseline) and potential adverse events (emergency department visits, opioid overdose, all-cause mortality, and benzodiazepine prescription fills) in the month after dose reduction by sex and age group were examined by including interaction terms in logistic regression models. RESULTS:Of the 600,234 dose reduction periods, 346,733 were among women, with a mean age of 57.5 (SD=13.2) years for women and 56.7 (SD=12.1) years for men. Associations between dose reduction levels and potential adverse events did not differ significantly by sex, but differed by age for emergency department visits: patients aged 40-64 and ≥65 years with dose reductions of 30% to <100% had lower odds than those aged 19-39 years (adjusted ratio of OR=0.87, CI=0.80, 0.96; adjusted ratio of OR=0.82, CI=0.74, 0.91, respectively). CONCLUSIONS:Patients aged <40 years may benefit from closer monitoring in the month after dose reduction, given their higher odds of an emergency department visit.

BackgroundArea-level credit scores (the mean of credit scores for persons in a community) may be a unique indicator of community-level socioeconomic conditions associated with health outcomes. We analysed community credit scores (CCS) in association with new onset type 2 diabetes (T2D) across a geographically heterogeneous region of Pennsylvania and evaluated whether associations were independent of community socioeconomic deprivation (CSD), which is known to be related to T2D risk.MethodsIn a nested case–control study, we used medical records to identify 15 888 T2D cases from diabetes diagnoses, medication orders and laboratory test results and 79 435 diabetes-free controls frequency matched on age, sex and encounter year. CCS was derived from Equifax VantageScore V.1.0 data and categorised as ‘good’, ‘high fair’, ‘low fair’ and ‘poor’. Individuals were geocoded and assigned the CCS of their residential community. Logistic regression models adjusted for confounding variables and stratified by community type (townships (rural/suburban), boroughs (small towns) and city census tracts). Independent associations of CSD were assessed through models stratified by high/low CSD and high/low CCS.ResultsCompared with individuals in communities with ‘high fair’ CCS, those with ‘good’ CCS had lower T2D odds (42%, 24% and 12% lower odds in cities, boroughs and townships, respectively). Stratified models assessing independent effects of CCS and CSD showed mainly consistent associations, indicating each community-level measure was independently associated with T2D.ConclusionCCS may capture novel, health-salient aspects of community socioeconomic conditions, though questions remain regarding the mechanisms by which it influences T2D and how these differ from CSD.

BackgroundInformation 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. ObjectiveTo 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. MethodsWe 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. ResultsThe 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. ConclusionsImplementation 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.

Background:Understanding geographic disparities in type 2 diabetes (T2D) requires approaches that account for communities' multidimensional nature. Methods:In an electronic health record nested case-control study, we identified 15,884 cases of new-onset T2D from 2008 to 2016, defined using encounter diagnoses, medication orders, and laboratory test results, and frequency-matched controls without T2D (79,400; 65,069 unique persons). We used finite mixture models to construct community profiles from social, natural, physical activity, and food environment measures. We estimated T2D odds ratios (OR) with 95% confidence intervals (CI) using logistic generalized estimating equation models, adjusted for sociodemographic variables. We examined associations with the profiles alone and combined them with either community type based on administrative boundaries or Census-based urban/rural status. Results:We identified four profiles in 1069 communities in central and northeastern Pennsylvania along a rural-urban gradient: "sparse rural," "developed rural," "inner suburb," and "deprived urban core." Urban areas were densely populated with high physical activity resources and food outlets; however, they also had high socioeconomic deprivation and low greenness. Compared with "developed rural," T2D onset odds were higher in "deprived urban core" (1.24, CI = 1.16-1.33) and "inner suburb" (1.10, CI = 1.04-1.17). These associations with model-based community profiles were weaker than when combined with administrative boundaries or urban/rural status. Conclusions:Our findings suggest that in urban areas, diabetogenic features overwhelm T2D-protective features. The community profiles support the construct validity of administrative-community type and urban/rural status, previously reported, to evaluate geographic disparities in T2D onset in this geography.

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 similar 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.

Objective: To examine associations between Theory of Planned Behavior constructs related to parent engagement in health-promoting behaviors and food resource management with household food security status. Methods: A cross-sectional secondary analysis of baseline data from 1622 parents enrolled in the ENCIRCLE study, a pragmatic cluster-randomized controlled trial to prevent obesity among preschool-aged children from rural lower-income families. Logistic regression models examined associations between parent engagement in health-promoting behaviors (attitude, subjective norm, perceived behavioral control) and food resource management (self-confidence, behaviors) with household food security status. Results: Parents with greater perceived behavioral control (OR=1.44, CI:1.14-1.82) and food resource management self-confidence (OR=2.08, CI:1.80-2.41) had significantly higher odds of experiencing household food security in adjusted models. Conclusions and Implications: Findings suggest that perceived behavioral control and food resource management self-confidence may help safeguard lower-income families. Health promotion efforts should target these factors to help parents engage in health-promoting behaviors during times of economic hardship. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Project Program Award (CER-2019C1-16040). All statements in this report, including its findings and conclusions, are solely those of the authors and do not necessarily represent the views of the PCORI, its Board of Governors or Methodology Committee. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/ Institutional Review Board of Geisinger, a large integrated health system, gave ethical approval for this work and this work is registered with ClinicalTrials.gov ([NCT04406441][1]). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes De-identified data are available upon reasonable request to the authors and with a data sharing agreement in place. [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT04406441&atom=%2Fmedrxiv%2Fearly%2F2024%2F08%2F23%2F2024.08.22.24312452.atom

Multi-site and multi-organizational teams are increasingly common in epidemiologic research; however, there is a lack of standards or best practices for achieving success in collaborative research networks in epidemiology. We summarize our experiences and lessons learned from the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a collaborative agreement between the Centers for Disease Control and Prevention and research teams at Drexel University, New York University, Johns Hopkins University and Geisinger, and the University of Alabama at Birmingham. We present a roadmap for success in collaborative epidemiologic research, with recommendations focused on the following areas to maximize efficiency and success in collaborative research agreements: 1) operational and administrative considerations; 2) data access and sharing of sensitive data; 3) aligning network research aims; 4) harmonization of methods and measures; and 5) dissemination of findings. Future collaborations can be informed by our experiences and ultimately dedicate more resources to achieving scientific aims and efficiently disseminating scientific work products.

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.

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.

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 similar to 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.

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.

Blue space exposure may benefit health and well-being, but salutogenic effects remain underexplored in non-coastal and non-urban areas, as do correlates of visit frequency to freshwater blue space (FBS). We mailed a questionnaire to adults in 40 small towns in central and northeast Pennsylvania, USA, with varying proximity to the region’s primary waterbody to assess FBS visits, benefits and barriers of visiting, restoration experienced, and mental health and well-being (perceived stress, general mental health, life satisfaction). We used multivariate multinomial regression to examine predictors of FBS visit frequency and linear and logistic regression to evaluate associations of FBS visit frequency with health outcomes, using mixed effects models that accounted for spatial clustering. Of 10,871 mailed questionnaires, 1,122 individuals (11%) responded and provided information to characterize FBS visits. Responders were older than non-responders but did not differ by sex or community setting. Nearly one-fifth (19%) never visited FBS, and 27%, 35%, and 19% reported low, moderate, and high visit frequency, respectively. Correlates of visit frequency included perceived proximity, higher education, younger age, better self-reported health, and more frequent physical activity. Relaxation/stress-relief was identified as the most important benefit to visiting FBS, and among the 868 individuals who visited FBS, restoration was associated with visit frequency. FBS visit frequency was associated with perceived stress, but not mental health or life satisfaction. Findings highlight the socioeconomic patterning of FBS visits, the importance of access in facilitating visits, and reveal opportunities to develop FBS access points that increase its use, bringing potential restorative benefits.