Dispensaries and Medical Marijuana Certifications and Indications: Unveiling the Geographic Connections in Pennsylvania, USA.

Introduction: Pennsylvania opened its first medical marijuana (MMJ) dispensary in 2018. Qualifying conditions include six conditions determined to have no or insufficient evidence to support or refute MMJ effectiveness. We conducted a study to describe MMJ dispensary access in Pennsylvania and to determine whether dispensary proximity was associated with MMJ certifications and community demographics. Methods: Using data from the Pennsylvania Department of Health, we geocoded MMJ dispensary locations and linked them to US Census Bureau data. We created dispensary access measures from the population-weighted centroid of Zip Code Tabulation Areas (ZCTAs): distance to nearest dispensary and density of dispensaries within a 15-min drive. We evaluated associations between dispensary access and the proportion of adults who received MMJ certification and the proportion of certifications for low evidence conditions (amyotrophic lateral sclerosis, epilepsy, glaucoma, Huntington's disease, opioid use disorder, and Parkinson's disease) using negative binomial modeling, adjusting for community features. To evaluate associations racial and ethnic composition of communities and distance to nearest dispensary, we used logistic regression to estimate the odds ratios (OR) and 95% confidence intervals (CI), adjusting for median income. Results: Distance and density of MMJ dispensaries were associated with the proportion of the ZCTA population certified and the proportion of certifications for insufficient evidence conditions. Compared to ZCTAs with no dispensary within 15 min, the proportion of adults certified increased by up to 31% and the proportion of certifications for insufficient evidence decreased by up to 22% for ZCTAs with two dispensaries. From 2018 to 2021, the odds of being within five miles of a dispensary was up to 20 times higher in ZCTAs with the highest proportions of individuals who were not White (2019: OR: 20.14, CI: 10.7-37.8) and more than double in ZCTAs with the highest proportion of Hispanic individuals (2018: OR: 2.81, CI: 1.51-5.24), compared to ZCTAs with the lowest proportions. Conclusions: Greater dispensary access was associated with the proportions of certified residents and certifications for low evidence conditions. Whether these patterns are due to differences in accessibility or demand is unknown. Associations between community demographics and dispensary proximity may indicate MMJ access differences.

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.

Effect of age and rural residency on perceptions about SARS-CoV-2 pandemic and vaccination in kidney transplant recipients.

BACKGROUND: Transplant patients have poor outcomes in coronavirus-disease 2019 (COVID-19). The pandemic's effects on rural patients' overall care experience, attitudes to telemedicine, and vaccination are poorly understood. METHODS: We administered a cross-sectional survey to adult kidney transplant recipients in central Pennsylvania across four clinical sites between March 29, 2021 and June 2, 2021. We assessed the pandemic's impact on care access, telemedicine experience, attitudes toward preventive measures, vaccination, and variation by sociodemographic variables. RESULTS: Survey completion rate was 51% (303/594). Of these, 52.8% were rural residents. The most common impact was use of telemedicine (79.2%). Predominant barriers to telemedicine were lack of video devices (10.9%), perceived complexity (5.6%), and technical issues (5.3%). On a 0-10 Likert scale, the mean positive impression for telemedicine was 7.7; lower for patients with telephone-only versus video visits (7.0 vs. 8.2; p < .001), and age ≥60 years (7.4 vs. 8.1; p = .01) on univariate analyses. Time/travel savings were commonly identified (115/241, 47.7%) best parts of telemedicine and lack of personal connection (70/166, 42.2%) the worst. Only 68.9% had received any dose of COVID vaccination. The vaccinated group members were older (58.4 vs. 53.5 years; p = .007), and less likely rural (47.8% vs. 65.2%; p = .005). Common themes associated with vaccine hesitancy included concerns about safety (27/59, 46%), perceived lack of data (19/59, 32%), and distrust (17/59, 29%). At least one misconception about the vaccines or COVID-19 was quoted by 29% of vaccine-hesitant patients. CONCLUSIONS: Among respondents, the pandemic significantly impacted healthcare experience, especially in older patients in underserved communities. COVID-19 vaccination rate was relatively low, driven by misconceptions and lack of trust.

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.

Relations of peri-residential temperature and humidity in tick-life-cycle-relevant time periods with human Lyme disease risk in Pennsylvania, USA.

How weather affects tick development and behavior and human Lyme disease remains poorly understood. We evaluated relations of temperature and humidity during critical periods for the tick lifecycle with human Lyme disease. We used electronic health records from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014. Lyme disease cases (n = 9657) were frequency-matched (5:1) by year, age, and sex. Using daily weather data at ~4 km2 resolution, we created cumulative metrics hypothesized to promote (warm and humid) or inhibit (hot and dry) tick development or host-seeking during nymph development (March 1-May 31), nymph activity (May 1-July 30), and prior year larva activity (Aug 1-Sept 30). We estimated odds ratios (ORs) of Lyme disease by quartiles of each weather variable, adjusting for demographic, clinical, and other weather variables. Exposure-response patterns were observed for higher cumulative same-year temperature, humidity, and hot and dry days (nymph-relevant), and prior year hot and dry days (larva-relevant), with same-year hot and dry days showing the strongest association (4th vs. 1st quartile OR = 0.40; 95% confidence interval [CI] = 0.36, 0.43). Changing temperature and humidity could increase or decrease human Lyme disease risk.

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.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania.

OBJECTIVES: To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions. DESIGN: Nested case-control study within the open dynamic cohort of health system patients. SETTING: Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA. PARTICIPANTS AND ANALYSIS: We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types. RESULTS: Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds. CONCLUSIONS: Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.

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.

Utilizing patient geographic information system data to plan telemedicine service locations.

OBJECTIVE: To understand potential utilization of clinical services at a rural integrated health care system by generating optimal groups of telemedicine locations from electronic health record (EHR) data using geographic information systems (GISs). METHODS: This retrospective study extracted nonidentifiable grouped data of patients over a 2-year period from the EHR, including geomasked locations. Spatially optimal groupings were created using available telemedicine sites by calculating patients' average travel distance (ATD) to the closest clinic site. RESULTS: A total of 4027 visits by 2049 unique patients were analyzed. The best travel distances for site groupings of 3, 4, 5, or 6 site locations were ranked based on increasing ATD. Each one-site increase in the number of available telemedicine sites decreased minimum ATD by about 8%. For a given group size, the best groupings were very similar in minimum travel distance. There were significant differences in predicted patient load imbalance between otherwise similar groupings. A majority of the best site groupings used the same small number of sites, and urban sites were heavily used. DISCUSSION: With EHR geospatial data at an individual patient level, we can model potential telemedicine sites for specialty access in a rural geographic area. Relatively few sites could serve most of the population. Direct access to patient GIS data from an EHR provides direct knowledge of the client base compared to methods that allocate aggregated data. CONCLUSION: Geospatial data and methods can assist health care location planning, generating data about load, load balance, and spatial accessibility.

Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE).

There are currently no direct observation environmental audit tools that measure diverse aspects of the obesity-related environment efficiently and reliably in a variety of geographic settings. The goal was to develop a new instrument to reliably characterize the overall properties and features of rural, suburban, and urban settings along multiple dimensions. The Community Audit of Social, Civil, and Activity Domains in Diverse Environments (CASCADDE) is an iPad-based instrument that incorporates GPS coordinates and photography and comprises 214 items yielding seven summary indices. A comprehensive spatial sampling strategy, training manual, and supporting data analysis code were also developed. Random geospatial sampling using GIS was used to capture features of the community as a whole. A single auditor collected 510 observation points in 30 communities (2013-2015). This analysis was done in 2015-2016. Correlation coefficients were used to compare items and indices to each other and to standard measures. Multilevel unconditional means models were used to calculate intraclass correlation coefficients to determine if there was significant variation between communities. Results suggest that CASCADDE measures aspects of communities not previously captured by secondary data sources. Additionally, seven summary indices capture meaningful differences between communities based on 15 observations per community. Community audit tools such as CASCADDE complement secondary data sources and have the potential to offer new insights about the mechanisms through which communities affect obesity and other health outcomes.

Body mass index and the built and social environments in children and adolescents using electronic health records.

BACKGROUND: No prior studies in children have evaluated how age may modify relationships of the built and social environments with BMI, nor evaluated the range of scales and contexts over which places may influence health. PURPOSE: To systematically evaluate associations of 33 environmental measures in three domains (land use, physical activity, and social environments) with BMI in children and adolescents in five geographies. METHODS: A cross-sectional, multilevel analysis was completed in 2009-2010 of electronic health record data (2001-2008) from 47,769 children aged 5-18 years residing in a 31-county region of Pennsylvania. Associations of environmental measures with BMI were evaluated using 0.5-mile network buffers; census tracts; minor civil divisions (i.e., townships, boroughs, cities); a mixed definition of place (townships, boroughs, and census tracts in cities); and counties, overall and by age strata. RESULTS: Among all children, lower levels of community socioeconomic deprivation and greater diversity of physical activity establishments were associated with lower BMI. Associations of environmental measures differed by age, depending on scale and context. For example, higher population density was associated with lower BMI in older children; this effect was strongest in the larger geographies. Similarly, a lower level of county sprawl was associated with lower BMI in older children. CONCLUSIONS: Associations differed by age and definition of place, suggesting that the benefits of environmental intervention may not be uniform across the childhood age range. The study demonstrated the utility of using electronic patient information for large-scale, population-based epidemiologic research, a research area of growing interest and investment in the U.S.