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.

Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network.

Objective: Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations. Research design and methods: We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE. Results: Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities. Conclusions: The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.

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.

Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study.

Objective: Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. Methods: 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. Results: The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. Conclusion: The orthopaedic patient appears comfortable with AI joining the care team.

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.

Risk of new-onset and prevalent disease in chronic rhinosinusitis: A prospective cohort study.

BACKGROUND: Chronic rhinosinusitis (CRS) is accompanied by burdensome comorbid conditions. Understanding the relative timing of the onset of these conditions could inform disease prevention, detection, and management. OBJECTIVE: To evaluate the association between CRS and new-onset and prevalent asthma, noncystic fibrosis bronchiectasis (NCFBE), chronic obstructive pulmonary disease (COPD), gastroesophageal reflux disease (GERD), and obstructive sleep apnea (OSA). METHODS: We conducted a prospective cohort study among primary care patients using a detailed medical and symptom questionnaire in 2014 and again in 2020. We used questionnaire and electronic health record (EHR) data to determine CRS status: CRSSE (moderate to severe symptoms with EHR evidence), CRSE (limited symptoms with EHR evidence), CRSS (moderate to severe symptoms without EHR evidence), CRSneg (limited symptoms and no EHR evidence; reference). We evaluated the association between CRS status and new-onset and prevalent disease using logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: There were 7847 and 4445 respondents to the 2014 and 2020 questionnaires, respectively. CRSSE (vs CRSneg ) was associated with increased odds of new-onset asthma (OR, 1.74 [CI, 1.09-2.77), NCFBE (OR, 1.87 [CI, 1.12-3.13]), COPD (OR, 1.73 [CI, 1.14-2.68]), GERD (OR, 1.95 [CI, 1.61-2.35]), and OSA (OR, 1.91 [CI, 1.39-2.62]). Similarly, increased odds were observed for associations with the prevalence of these conditions. CONCLUSION: The findings from the study support further exploration of CRS as a target for the prevention and detection of asthma, NCFBE, COPD, GERD, and OSA.

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.

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.

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.

Association of community socioeconomic deprivation with evidence of reduced kidney function at time of type 2 diabetes diagnosis.

BACKGROUND: While there are known individual-level risk factors for kidney disease at time of type 2 diabetes diagnosis, little is known regarding the role of community context. We evaluated the association of community socioeconomic deprivation (CSD) and community type with estimated glomerular filtration rate (eGFR) when type 2 diabetes is diagnosed. METHODS: This was a retrospective cohort study of 13,144 adults with newly diagnosed type 2 diabetes in Pennsylvania. The outcome was the closest eGFR measurement within one year prior to and two weeks after type 2 diabetes diagnosis, calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equation. We used adjusted multinomial regression models to estimate associations of CSD (quartile 1, least deprivation) and community type (township, borough, city) with eGFR and used adjusted generalized estimating equation models to evaluate whether community features were associated with the absence of diabetes screening in the years prior to type 2 diabetes diagnosis. RESULTS: Of the participants, 1279 (9.7%) had hyperfiltration and 1377 (10.5%) had reduced eGFR. Women were less likely to have hyperfiltration and more likely to have reduced eGFR. Black (versus White) race was positively associated with hyperfiltration when the eGFR calculation was corrected for race but inversely associated without the correction. Medical Assistance (ever versus never) was positively associated with reduced eGFR. Higher CSD and living in a city were each positively associated (odds ratio [95% confidence interval]) with reduced eGFR (CSD quartiles 3 and 4 versus quartile 1, 1.23 [1.04, 1.46], 1.32 [1.11, 1.58], respectively; city versus township, 1.38 [1.15, 1.65]). These features were also positively associated with the absence of a type 2 diabetes screening measure. CONCLUSIONS: In a population-based sample, more than twenty percent had hyperfiltration or reduced eGFR at time of type 2 diabetes diagnosis. Individual- and community-level factors were associated with these outcomes.

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.

Risk Factors and Outcomes of Treatment Delays in Lyme Disease: A Population-Based Retrospective Cohort Study.

Background: Longer time between symptom onset and treatment of Lyme disease has been associated with poor outcomes. Reducing time-to-treatment requires knowledge of risks for treatment delays. We conducted a population-based study to evaluate factors associated with delayed treatment of Lyme disease and the relation between delayed treatment and post-treatment Lyme disease syndrome (PTLDS). Methods: We mailed questionnaires to 5,314 individuals with a Lyme disease diagnosis or blood test followed by an antibiotic order in the medical record of a Pennsylvania health system from 2015 to 2017. Analyses were confined to 778 respondents who reported that they were treated for Lyme disease within the past 5 years and reported a rash and/or a positive blood test for Lyme disease. Time-to-treatment was calculated as the sum of two windows before and after seeking care for Lyme disease symptoms: time to first medical contact and time under care. We used logistic regression to evaluate factors associated with delayed time-to-treatment in each time window (>14 days vs. ≤14 days) and the association between total time-to-treatment (>30 days vs. ≤30 days) and PTLDS. We used inverse probability weighting to calculate estimates for the study's source population (5,314 individuals sent questionnaires). Results: In the source population, 25% had time to first contact >14 days, 21% had time under care >14 days, and 31% had a total time-to-treatment >30 days. Being uninsured and attributing initial symptoms to something other than Lyme disease were positively associated with delayed time to first medical contact, while seeking care at an urgent care or emergency setting (vs. primary care) was negatively associated. Diagnoses between November and April, and the absence of rash were positively associated with delays. Individuals whose treatment was delayed, defined as time-to treatment >30 days had 2.26 (95% confidence interval: 1.25, 4.05) times the odds of PTLDS as those who were treated within 30 days of symptom onset. Conclusions: In a population-based study in Pennsylvania, one-third of Lyme disease patients reported delayed treatment, which was associated with PTLDS. To improve Lyme disease outcomes, prevention efforts should aim to reduce the time before and after seeking care.

Maternal risk factors associated with persistent placenta previa.

INTRODUCTION: Maternal risk factors associated with placenta previa are well documented in the literature. However, there are limited studies identifying maternal characteristics associated with the persistence of placenta previa. The objective of the study was to determine maternal characteristics associated with the persistent placenta previa. METHODS: A retrospective cohort study was conducted in which 705 pregnant women diagnosed with low-lying placenta or placenta previa between 17 and 24 weeks gestation were identified from a single institution between 2003 and 2017. The primary outcome included persistent placenta previa (i.e., persistent placental tissue within 2 cm of the internal os) at or after 36 weeks 0 days. Those with abnormal placentation (e.g., vasa previa, placenta accreta) or delivery prior to 36 weeks 0 days were excluded. Multivariable logistic regression modeling was utilized to determine significant maternal characteristics associated with persistent placenta previa among women diagnosed with either placenta previa or low-lying placenta. RESULTS: Women with a prior cesarean delivery were seven times more likely to have persistent placenta previa (odds ratio, 7.0, 95% confidence interval, 3.7-13.1). A history of intrauterine curettage or evacuation in the setting of placenta previa increases the likelihood of persistent placenta previa almost 3-fold (odds ratio, 2.5, 95% confidence interval, 1.3-5.0). DISCUSSION: To date, our study is the largest, retrospective cohort study assessing maternal risk factors associated with persistent placenta previa; and is the first to detect a statistically significant correlation between a history of intrauterine surgeries and persistent placenta previa.

A randomized controlled trial to assess operator radiation exposure from cardiac catheterization procedures using RAD BOARD® with standard pelvic shielding versus standard pelvic shielding alone.

OBJECTIVE: To study radiation exposure to the primary operator during diagnostic cardiac catheterizations using a radio-dense RAD BOARD® radial access arm board. BACKGROUND: The use of radial access for catheterization in the United States has increased from 1% in 2007 to 41% in 2018. Compared to femoral access, operator radiation exposure from radial access is similar or higher. The RAD BOARD radio-dense radial access arm board has been marketed as reducing radiation to operators by 44%. MATERIALS AND METHODS: We randomized 265 patients undergoing catheterization via right radial access to standard pelvic lead drape shielding (nonboard group) versus RAD BOARD in addition to pelvic drape (board group). Operator radiation exposure was measured using Landauer Microstar nanoDot™ badges worn by the operator. RESULTS: Board and nonboard groups were similar with respect to demographic and procedural variables. Mean operator dose per case was higher in the board group (.65mSieverts) than in the nonboard group (.56mSieverts, P < 0.0001). In sub-group analyses, radiation doses were higher in the board group compared to the nonboard group in patients across all body mass index groups (P < 0.03). In multivariate analysis, operator dose correlated with use of the RAD BOARD more closely than any other variable (P < 0.001). Post hoc analysis of the table setup with RAD BOARD revealed that use of RAD BOARD prevented placement of a shield normally inserted into the top of the standard below-table shield. CONCLUSION: RAD BOARD with the pelvic shield was associated with higher radiation exposure to the operator compared with pelvic shield alone, likely due to inability to use standard radiation shielding along with the RAD BOARD.

Radiologic sinus inflammation and symptoms of chronic rhinosinusitis in a population-based sample.

BACKGROUND: Chronic rhinosinusitis (CRS) epidemiology has been largely studied using symptom-based case definitions, without assessment of objective sinus findings. OBJECTIVE: To describe radiologic sinus opacification and the prevalence of CRS, defined by the co-occurrence of symptoms and sinus opacification, in a general population-based sample. METHODS: We collected questionnaires and sinus CT scans from 646 participants selected from a source population of 200 769 primary care patients. Symptom status (CRSS ) was based on guideline criteria, and objective radiologic inflammation (CRSO ) was based on the Lund-Mackay (L-M) score using multiple L-M thresholds for positivity. Participants with symptoms and radiologic inflammation were classified as CRSS+O . We performed negative binomial regression to assess factors associated with L-M score and logistic regression to evaluate factors associated with CRSS+O . Using weighted analysis, we calculated estimates for the source population. RESULTS: The proportion of women with L-M scores ≥ 3, 4, or 6 (CRSO ) was 11.1%, 9.9%, and 5.7%, respectively, and 16.1%, 14.6%, and 8.7% among men. The respective proportion with CRSS+O was 1.7%, 1.6%, and 0.45% among women and 8.8%, 7.5%, and 3.6% among men. Men had higher odds of CRSS+O compared to women. A greater proportion of men (vs women) had any opacification in the frontal, anterior ethmoid, and sphenoid sinuses. CONCLUSION: In a general population-based sample in Pennsylvania, sinus opacification was more common among men than in women and opacification occurred in different locations by sex. Male sex, migraine headache, and prior sinus surgery were associated with higher odds of CRSS+O .

Epidemiology of Lyme disease in Pennsylvania 2006-2014 using electronic health records.

Lyme disease is the most common vector-borne disease in the United States. Electronic health record (EHR)-based research on Lyme disease is limited. We used Geisinger EHR data from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014 to compare EHR-based Lyme disease incidence rates to surveillance incidence rates, evaluate individual and community risk factors for incident Lyme disease, and to characterize the proportion of cases with diagnoses consistent with post-treatment Lyme disease syndrome in the EHR (PTLDSEHR). We primarily identified Lyme disease cases using diagnosis codes, serologic testing order codes, and medication orders but also completed subgroup analyses among those with positive serology and those with both diagnosis code and antibiotic treatment. We compared annual incidence rates from the EHR to surveillance by age, sex, and county. In case-control analyses, we compared cases to randomly selected controls (5:1) frequency-matched on year, age, and sex. We identified 9657 cases of Lyme disease, including 1791 cases with positive serology and 4992 cases with both diagnosis code and antibiotic treatment. Annual incidence rates in the EHR were 4.25-7.43 times higher than surveillance. In adjusted analyses, white non-Hispanic race/ethnicity (vs. black, Hispanic, or other) was associated with higher odds of Lyme disease (odds ratio [OR]: 2.06, 95% confidence interval [CI]: 1.73-2.44). Medical Assistance insurance use (always vs. never; OR: 0.77, 95% CI: 0.68-0.88), and higher community-level socioeconomic deprivation (quartile 4 vs. 1 OR: 0.50 (95% CI: 0.42-0.59) were associated with lower odds of Lyme disease. Within 4-52 weeks after Lyme disease diagnosis, 20.8% (n = 735) of cases with a diagnosis code and treatment had a diagnosis of malaise or fatigue, pain, or cognitive difficulties not present in the past 26 weeks. These results highlight the utility of EHR data for epidemiologic research on Lyme disease for case-finding, surveillance, risk factor evaluation, and characterization of PTLDS using EHR data.

Associations of Four Community Factors With Longitudinal Change in Hemoglobin A1c Levels in Patients With Type 2 Diabetes.

OBJECTIVE: To evaluate associations of community factors with glycated hemoglobin (HbA1c). RESEARCH DESIGN AND METHODS: We identified patients with type 2 diabetes who had an HbA1c ≥7.5% (58 mmol/mol) and subsequent HbA1c testing within 90-270 days. We used mixed-effect models to assess whether treatment intensification (TI) and community domains (community socioeconomic deprivation [CSD], food availability, fitness assets, and utilitarian physical activity favorability [quartiled]) were associated with HbA1c change over 6 and 24 months, controlling for demographics, HbA1c, BMI, and time with evidence of type 2 diabetes. We evaluated whether community domains modified associations of TI with HbA1c change using cross product terms. RESULTS: There were 15,308 patients with 69,818 elevated HbA1c measures. The average reduction in HbA1c over 6 months was 0.07% less in townships with a high level of CSD (third quartile versus the first). Reductions were 0.10% greater for HbA1c in townships with the best food availability (versus worst). HbA1c reductions were 0.17-0.19% greater in census tracts in the second and third quartiles of utilitarian physical activity favorability versus the first. The association of TI with 6-month HbA1c change was weaker in townships and boroughs with the worst CSD (versus best) and in boroughs with the best fitness assets (versus worst). The association of TI with 24-month HbA1c change was weaker in census tracts with the worst CSD (versus third quartile) and strongest in census tracts most favorable for utilitarian physical activity (versus worst). CONCLUSIONS: Community domains were associated with HbA1c change and blunted TI effectiveness.

Clinical Characteristics of Patients with Chronic Rhinosinusitis with Nasal Polyps, Asthma, and Aspirin-Exacerbated Respiratory Disease.

BACKGROUND: Aspirin-exacerbated respiratory disease (AERD) comprises the triad of chronic rhinosinusitis with nasal polyps (CRSwNP), asthma, and intolerance to inhibitors of the cyclooxygenase-1 (COX-1) enzyme. The prevalence of AERD remains unclear, and few studies have compared the clinical characteristics of patients with AERD to those with CRSwNP alone, asthma alone, or both CRSwNP and asthma. OBJECTIVE: To determine the prevalence of AERD within a tertiary care setting, and to identify unique clinical features that could distinguish these patients from those with both CRSwNP and asthma or with CRSwNP alone. METHODS: Electronic medical records of patients at Northwestern in Chicago, Illinois, were searched by computer algorithm and then manual chart review to identify 459 patients with CRSwNP alone, 412 with both CRSwNP and asthma, 171 with AERD, and 300 with asthma only. Demographic and clinical features including sex, atopy, and sinus disease severity were characterized. RESULTS: The prevalence of AERD among patients with CRSwNP was 16%. Patients with AERD had undergone 2-fold more sinus surgeries (P < .001) and were significantly younger at the time of their first surgery (40 ± 13 years) than were patients with CRSwNP (43 ± 14 years; P < .05). Atopy was significantly more prevalent in patients with AERD (84%) or asthma (85%) than in patients with CRSwNP (66%, P < .05). More patients with AERD (13%) had corticosteroid-dependent disease than patients with both CRSwNP and asthma (4%, P < .01) or asthma (1%, P < .001). CONCLUSIONS: AERD is common among patients with CRSwNP; even though patients with AERD have CRSwNP and asthma, the clinical course of their disease is not the same as of patients who have CRSwNP and asthma but are tolerant to COX-1 inhibitors.