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Since its inception in 1991, the mission of the National Breast and Cervical Cancer Early Detection Program's (NBCCEDP) mission is to improve access to mammography. This program has demonstrated evidence showing that it has improved breast cancer screening rates for women who are uninsured and underinsured. However, the literature has shown that NBCCEDP screenings are decreasing, and only reach a portion of eligible women. Reliable estimates at the sub-county level are needed to identify and reach eligible women. Our work builds upon previous estimates by integrating uninsured and insurance status into spatially adaptive filters. We use spatially adaptive filters to create small area estimates of standardized incidence ratios describing the utilization rate of NBCCEDP services in Minnesota. We integrate the American Community Survey (2010–2014) insurance status data to account for the percentage that an individual is uninsured. We test five models that integrate insurance status by age, sex, and race/ethnicity. Our composite model, which adjusts for age, sex, and race/ethnicity insurance statuses, reduces 95% of the estimation error. We estimate that there approximately 49,913.7 women eligible to receive services for Minnesota. We also create small geography (i.e., county and sub-county) estimates for Minnesota. The integration of the insurance data improved our utilization estimate. The development of these methods will allow state programs to more efficiently use their resources and understand their reach. [ABSTRACT FROM AUTHOR]

To estimate associations of residential mobility with body mass index (BMI), physical activity, and diet and whether associations differ across demographics. Longitudinal cohort with 4 waves of survey follow-up over 15 years. A total of 2,110 adolescents and young adults originally from the Twin Cities of Minnesota responded to at least 2 waves of follow-up, beginning at ages 15 to 23 years. Self-reported BMI, physical activity, fast food consumption, breakfast frequency, sugary drink consumption, fruit and vegetable consumption, and screen time. Each outcome was modeled as a continuous variable using hierarchical linear regression. Residential mobility—change in residential address—was the main effect of interest. Models adjusted for demographics, marriage during follow-up, and previous level of the outcome. Inverse propensity weights accounted for loss to follow up. No weight-related outcomes differed between movers and nonmovers in the whole sample. When examining effect modification by age, as participants aged, moving was increasingly associated with improvements in weight-related outcomes, particularly BMI. Results suggest that moving may be associated with poorer weight-related outcomes during a brief window from late teens and early-20s and less associated with weight-related outcomes in the mid-20s and 30s. [ABSTRACT FROM AUTHOR]