Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.

PURPOSE: Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive. METHODS: We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors. RESULTS: Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%. CONCLUSION: These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.

Impact of Medicaid Expansion on Breast Cancer Diagnosis and Treatment in Southern States.

BACKGROUND: Medicaid expansion impacted patients when assessed at a national level. However, of the 32 states that expanded Medicaid, only three were Southern states. Whether results apply to Southern states that share similar geopolitical perspectives remains elusive. We aimed to assess the impact of Medicaid expansion on breast cancer diagnosis and treatment in 8 Southern states in the US. STUDY DESIGN: We identified uninsured or Medicaid patients (age 40 to 64 years) diagnosed with invasive breast cancer from 2011 to 2018 in Southern states from the North American Association of Central Cancer Registries-Cancer in North America Research Dataset. Medicaid-expanded states ([MES], Louisiana, Kentucky, Arkansas) were compared with non-MES ([NMES], Tennessee, Alabama, Mississippi, Texas, Oklahoma) using multivariate logistic regression and differences-in-differences analyses during pre- and postexpansion periods; p < 0.05 was considered statistically significant. RESULTS: Among 21,974 patients, patients in MES had increased odds of Medicaid insurance by 43% (odds ratio 1.43, p < 0.01) and decreased odds of distant-stage disease by 7% (odds ratio 0.93, p = 0.03). After Medicaid expansion, Medicaid patients increased by 10.6% in MES (Arkansas, Kentucky), in contrast to a 1.3% decrease in NMES (differences-in-differences 11.9%, p < 0. 0001, adjusting for age, race/ethnicity, rural-urban status, and poverty status). MES (Arkansas, Kentucky) had 2.3% fewer patients diagnosed with distant-stage disease compared with a 0.5% increase in NMES (differences-in-differences 2.8%, p = 0.01, after adjustment). Patients diagnosed in MES had higher odds of receiving treatment (odds ratio 2.27, p = 0.03). CONCLUSIONS: Unlike NMES, MES experienced increased Medicaid insured, increased treatment, and decreased distant-stage disease at diagnosis. Medicaid expansion in the South leads to earlier and more comprehensive treatment of breast cancer.

Time to Treatment Initiation for Breast Cancer During the 2020 COVID-19 Pandemic.

PURPOSE: The COVID-19 pandemic has posed significant pressures on healthcare systems, raising concern that related care delays will result in excess cancer-related deaths. Because data regarding the impact on patients with breast cancer are urgently needed, we aimed to provide a preliminary estimate of the impact of COVID-19 on time to treatment initiation (TTI) for patients newly diagnosed with breast cancer cared for at a large academic center. METHODS: We conducted a retrospective study of patients with newly diagnosed early-stage breast cancer between January 1, 2020, and May 15, 2020, a time period during which care was affected by COVID-19, and an unaffected cohort diagnosed between January 1, 2018 and May 15, 2018. Outcomes included patient volume, TTI, and initial treatment modality. Adjusted TTI was compared using multivariable linear regression. RESULTS: Three hundred sixty-six patients were included. There was an 18.8% decrease in patient volume in 2020 (n = 164) versus 2018 (n = 202). There was no association between time of diagnosis (pre-COVID-19 or during COVID-19) and adjusted TTI (P = .926). There were fewer in situ diagnoses in the 2020 cohort (P = .040). There was increased use of preoperative systemic therapy in 2020 (43.9% overall, 20.7% chemotherapy, and 23.2% hormonal therapy) versus 2018 (16.4% overall, 12.4% chemotherapy, and 4.0% hormonal therapy) (P < .001). CONCLUSION: TTI was maintained among patients diagnosed and treated for breast cancer during the COVID-19 pandemic at a single large academic center. There was a decrease in patient volume, specifically in patients with in situ disease and a shift in initial therapy toward the use of preoperative hormonal therapy.