Associations of Cost Sharing With Rheumatoid Arthritis Disease Burden.

OBJECTIVE: To evaluate the regional variation of cost sharing and associations with rheumatoid arthritis (RA) disease burden in the US. METHODS: Patients with RA from rheumatology practices in Northeast, South, and West US regions were evaluated. Sociodemographics, RA disease status, and comorbidities were collected, and Rheumatic Disease Comorbidity Index (RDCI) score was calculated. Primary insurance types and copay for office visits (OVs) and medications were documented. Univariable pairwise differences between regions were conducted, and multivariable regression models were estimated to evaluate associations of RDCI with insurance, geographical region, and race. RESULTS: In a cohort of 402 predominantly female, White patients with RA, most received government versus private sponsored primary insurance (40% vs. 27.9%). Disease activity and RDCI were highest for patients in the South region, where copays for OVs were more frequently more than $25. Copays for OVs and medications were less than $10 in 45% and 31.8% of observations, respectively, and more prevalent in the Northeast and West patient subsets than in the South subset. Overall, RDCI score was significantly higher for OV copays less than $10 as well as for medication copays less than $25, both independent of region or race. Additionally, RDCI was significantly lower for privately insured than Medicare individuals (RDCI -0.78, 95% CI [-0.41 to -1.15], P < 0.001) and Medicaid (RDCI -0.83, 95% CI [-0.13 to -1.54], P = 0.020), independent of region and race. CONCLUSION: Cost sharing may not facilitate optimum care for patients with RA, especially in the Southern regions. More support may be required of government insurance plans to accommodate patients with RA with a high disease burden.

Geographic Variation in Disease Burden and Mismatch in Care of Patients With Rheumatoid Arthritis in the United States.

OBJECTIVE: Our objective was to evaluate the factors associated with regional variation of rheumatoid arthritis (RA) disease burden in the US. METHODS: In a retrospective cohort analysis of Rheumatology Informatics System for Effectiveness (RISE) registry data, seropositivity, RA disease activity (Clinical Disease Activity Index [CDAI], Routine Assessment of Patient Index Data-version 3 [RAPID3]), socioeconomic status (SES), geographic region, health insurance type, and comorbidity burden were recorded. An Area Deprivation Index score of more than 80 defined low SES. Median travel distance to practice sites' zip codes was calculated. Linear regression was used to analyze associations between RA disease activity and comorbidity adjusting for age, sex, geographic region, race, and insurance type. RESULTS: Enrollment data for 184,722 patients with RA from 182 RISE sites were analyzed. Disease activity was higher in African American patients, in those from Southern regions, and in those with Medicaid or Medicare coverage. Greater comorbidity was prevalent in patients in the South and those with Medicare or Medicaid coverage. There was moderate correlation between comorbidity and disease activity (Pearson coefficient: RAPID3 0.28, CDAI 0.15). High-deprivation areas were mainly in the South. Less than 10% of all participating practices cared for more than 50% of all Medicaid recipients. Patients living more than 200 miles away from specialist care were located mainly in Southern and Western regions. CONCLUSION: A disproportionately large portion of socially deprived, high comorbidity, and Medicaid-covered patients with RA were cared for by a minority of rheumatology practices. Studies are needed in high-deprivation areas to establish more equitable distribution of specialty care for patients with RA.

Mapping bare ground in New Zealand hill-country agriculture and forestry for soil erosion risk assessment: An automated satellite remote-sensing method.

Removing vegetation cover from hill-slope land increases risk for soil erosion and delivery of sediment to waterways. In New Zealand's productive landscapes, clear-fell harvesting of forestry blocks and winter forage grazing by agricultural livestock are two significant causes of vegetation removal. Bare ground exposed by these activities varies annually and seasonally in location and spatial extent. Modelling soil erosion therefore requires temporally and spatially explicit mapping of this bare ground. We have developed an automated mapping method using time-series satellite imagery, thereby enabling wide-area coverage and ease of updating. The temporal analysis identifies land use along with the period of vegetation removal. It produces results per land parcel (in vector format) for use in a Geographic Information System. We present a description of our method, national maps and statistics of bare ground extent in New Zealand's hill-country forestry and winter forage grazing land in 2018, and an assessment of accuracy. The attributes of the mapped land parcels are designed for input into a soil erosion estimation model such as the New Zealand Universal Soil Loss Equation.