Impact of Dosing Conversion From Basal Insulin to Follow-On Insulin Glargine.

BACKGROUND: Several basal insulins have recently come to market including follow-on insulin glargine (Basaglar®). Currently, there is no real-world data published on the implications of conversion to Basaglar on dosing or glycemic control. OBJECTIVE: To identify differences in basal insulin dosing requirements, hemoglobin A1c (HbA1c), and incidence of hypoglycemia or weight gain when converting a patient to Basaglar from another basal insulin. METHODS: Single-center, retrospective chart review at an academic medical center. All patients prescribed Basaglar between December 15, 2016, and August 31, 2017 were included for review if converted from another basal insulin. PRIMARY OUTCOME: Difference in basal insulin requirements in both units/d and units/kilogram (kg)/d after conversion to Basaglar. SECONDARY OUTCOME: Change in HbA1c and weight. RESULTS: Mean basal insulin dose was 38.4 ± 26.3 units/d pre-conversion and 40.5 ± 29.8 units/d post-conversion (P = .031). Results were significant for patients with type 2 diabetes mellitus (T2DM; pre-conversion basal dose 34.6 ± 24.3 units/d; post-conversion basal dose 37.6± 29.0 units/d; P = .009). Weight-based dosing changed from 0.37 ± 0.25 units/kg/d pre-conversion to 0.39 ± 0.29 units/kg/d post-conversion (P = .056) and was significant for patients with T2DM (P = .040). A nonsignificant decrease in HbA1c was seen (-0.14% ± 1.24%; P = .142). There was no difference seen in weight (111.6 ± 46.3 kg vs 111.7 ± 46.9 kg; P = .662). CONCLUSION: Patients with diabetes require similar basal insulin doses upon conversion to Basaglar. Clinicians should monitor blood glucose closely during basal insulin transition.

Spatial Access to Primary Care Providers in Appalachia: Evaluating Current Methodology.

PURPOSE: The goal of this research was to examine spatial access to primary care physicians in Appalachia using both traditional access measures and the 2-step floating catchment area (2SFCA) method. Spatial access to care was compared between urban and rural regions of Appalachia. METHODS: The study region included Appalachia counties of Pennsylvania, Ohio, Kentucky, and North Carolina. Primary care physicians during 2008 and total census block group populations were geocoded into GIS software. Ratios of county physicians to population, driving time to nearest primary care physician, and various 2SFCA approaches were compared. RESULTS: Urban areas of the study region had shorter travel times to their closest primary care physician. Provider to population ratios produced results that varied widely from one county to another because of strict geographic boundaries. The 2SFCA method produced varied results depending on the distance decay weight and variable catchment size techniques chose. 2SFCA scores showed greater access to care in urban areas of Pennsylvania, Ohio, and North Carolina. CONCLUSION: The different parameters of the 2SFCA method-distance decay weights and variable catchment sizes-have a large impact on the resulting spatial access to primary care scores. The findings of this study suggest that using a relative 2SFCA approach, the spatial access ratio method, when detailed patient travel data are unavailable. The 2SFCA method shows promise for measuring access to care in Appalachia, but more research on patient travel preferences is needed to inform implementation.

Predicting Late-stage Breast Cancer Diagnosis and Receipt of Adjuvant Therapy: Applying Current Spatial Access to Care Methods in Appalachia.

PURPOSE: The 2-step floating catchment area (2SFCA) method of measuring access to care has never been used to study cancer disparities in Appalachia. First, we evaluated the 2SFCA method in relation to traditional methods. We then examined the impact of access to mammography centers and primary care on late-stage breast cancer diagnosis and receipt of adjuvant hormonal therapy. METHODS: Cancer registries from Pennsylvania, Ohio, Kentucky, and North Carolina were linked with Medicare data to identify the stage of breast cancer diagnosis for Appalachia women diagnosed between 2006 and 2008. Women eligible for adjuvant therapy had stage I, II, or III diagnosis; mastectomy or breast-conserving surgery; and hormone receptor-positive breast cancers. Geographically weighted regression was used to explore nonstationarity in the demographic and spatial access predictor variables. RESULTS: Over 21% of 15,299 women diagnosed with breast cancer had late-stage (stages III-IV) diagnosis. Predictors included age at diagnosis [odds ratio (OR)=0.86; P<0.001], insurance status (OR=1.32; P<0.001), county primary care to population ratio (OR=0.95; P<0.001), and primary-care 2SFCA score (OR=0.96; P=0.006). Only 46.9% of eligible women received adjuvant hormonal therapy, and predictors included comorbidity status (OR=1.18; P=0.047), county economic status (OR=1.32; P=0.006), and mammography center 2SFCA scores (OR=1.12; P=0.021). CONCLUSIONS: Methodologically, the 2SFCA method offered the greatest predictive validity of the access measures examined. Substantively, rates of late-stage breast cancer diagnosis and adjuvant hormonal therapy are substandard in Appalachia.

Impact of a passive social marketing intervention in community pharmacies on oral contraceptive and condom sales: a quasi-experimental study.

BACKGROUND: Almost 50% of pregnancies in the United States are unwanted or mistimed. Notably, just over one-half of unintended pregnancies occurred when birth control was being used, suggesting inappropriate or poor use or contraceptive failure. About two-thirds of all women who are of reproductive age use contraceptives, and oral hormonal contraceptives remain the most common contraceptive method. Often, contraceptive products are obtained in community pharmacies. The purpose of this study was to determine whether a pharmacy-based intervention would impact sales of contraceptive products in pharmacies. METHODS: This study was conducted in Iowa and used a quasi-experimental design including 55 community pharmacies (independent and grocery) in 12 counties as the intervention and 32 grocery pharmacies in 10 counties as a comparison group. The passive intervention was focused towards 18-30 year old women who visited community pharmacies and prompted those of childbearing age to "plan your pregnancy" and "consider using birth control". The intervention was delivered via educational tri-fold brochures, posters and 'shelf talkers.' Data sources for evaluation were contraceptive sales from intervention and comparison pharmacies, and a mixed negative binomial regression was used with study group*time interactions to examine the impact of the intervention on oral contraceptive and condom sales. Data from 2009 were considered baseline sales. RESULTS: From 2009 to 2011, condom sales decreased over time and oral contraceptives sales showed no change. Overall, the units sold were significantly higher in grocery pharmacies than in independent pharmacies for both contraceptive types. In the negative binomial regression for condoms, there was an overall significant interaction between the study group and time variables (p = 0.003), indicating an effect of the intervention, and there was a significant slowing in the drop of sales at time 3 in comparison with time 1 (p < 0.001). There was a statistically significant association between pharmacy type and study group, where the independent intervention pharmacies had a higher proportion of stores with increases in condom sales compared to grocery pharmacies in the intervention or comparison group. CONCLUSIONS: A passive community pharmacy-based public health intervention appeared to reduce the decrease in condom sales from baseline, particularly in independent pharmacies, but it did not impact oral contraceptive sales.