OB Nest randomized controlled trial: a cost comparison of reduced visit compared to traditional prenatal care.

BACKGROUND: Traditional prenatal care includes up to 13 in person office visits, and the cost of this care is not well-described. Alternative models are being explored to better meet the needs of patients and providers. OB Nest is a telemedicine-enhanced program with a reduced frequency of in-person prenatal visits. The cost implications of connected care services added to prenatal care packages are unclear. METHODS: Using data from the OB Nest randomized, controlled trial we analyzed the provider and staff time associated with prenatal care in the traditional and OB Nest models. Fewer visits were required for OB Nest, but given the compensatory increase in connected care activity and supplies, the actual cost difference is not known. Nursing and provider staff time was prospectively recorded for all patients enrolled in the OB Nest clinical trial. Published 2015 national wages for healthcare workers were used to calculate the actual labor cost of providing either traditional or OB Nest prenatal care in 2015 US dollars. Overhead expenses and opportunity costs were not considered. RESULTS: Total provider cost was decreased caring for the OB Nest participants, but nursing cost was increased. OB Nest care required an average of 160.8 (+/- 45.0) minutes provider time and 237 (+/- 25.1) minutes nursing time, compared to 215.0 (+/- 71.6) and 99.6 (+/- 29.7) minutes for traditional prenatal care (P < 0.01). This translated into decreased provider cost and increased nursing cost (P < 0.01). Supply costs increased, travel costs declined, and overhead costs declined in the OB Nest model. CONCLUSIONS: In this trial, labor cost for OB Nest prenatal care was 34% higher than for traditional prenatal care. The increased cost is largely attributable to additional nursing connected care time, and in some practice settings may be offset by decreased overhead costs and increased provider billing opportunities. Future efforts will be focused on development of digital solutions for some routine nursing tasks to decrease the overall cost of the model. TRIAL REGISTRATIONS: ClinicalTrials.gov Identifier: NCT02082275 .

Association between an individual housing-based socioeconomic index and inconsistent self-reporting of health conditions: a prospective cohort study in the Mayo Clinic Biobank.

OBJECTIVE: Using surveys to collect self-reported information on health and disease is commonly used in clinical practice and epidemiological research. However, the inconsistency of self-reported information collected longitudinally in repeated surveys is not well investigated. We aimed to investigate whether a socioeconomic status based on current housing characteristics, HOUsing-based SocioEconomic Status (HOUSES) index linking current address information to real estate property data, is associated with inconsistent self-reporting. STUDY SETTING AND PARTICIPANTS: We performed a prospective cohort study using the Mayo Clinic Biobank (MCB) participants who resided in Olmsted County, Minnesota, USA, at the time of enrolment between 2009 and 2013, and were invited for a 4-year follow-up survey (n=11 717). PRIMARY AND SECONDARY OUTCOME MEASURES: Using repeated survey data collected at the baseline and 4 years later, the primary outcome was the inconsistency in survey results when reporting prevalent diseases, defined by reporting to have 'ever' been diagnosed with a given disease in the baseline survey but reported 'never' in the follow-up survey. Secondary outcome was the response rate for the 4-year follow-up survey. RESULTS: Among the MCB participants invited for the 4-year follow-up survey, 8508/11 717 (73%) responded to the survey. Forty-three per cent had at least one inconsistent self-reported disease. Lower HOUSES was associated with higher inconsistency rates, and the association remained significant after pertinent characteristics such as age and perceived general health (OR=1.46; 95% CI 1.17 to 1.84 for the lowest compared with the highest HOUSES decile). HOUSES was also associated with lower response rate for the follow-up survey (56% vs 77% for the lowest vs the highest HOUSES decile). CONCLUSION: This study demonstrates the importance of using the HOUSES index that reflects current SES when using self-reporting through repeated surveys, as the HOUSES index at baseline survey was inversely associated with inconsistent self-report and the response rate for the follow-up survey.

Individual housing-based socioeconomic status predicts risk of accidental falls among adults.

PURPOSE: Accidental falls are a major public health concern among people of all ages. Little is known about whether an individual-level housing-based socioeconomic status measure is associated with the risk of accidental falls. METHODS: Among 12,286 Mayo Clinic Biobank participants residing in Olmsted County, Minnesota, subjects who experienced accidental falls between the biobank enrollment and September 2014 were identified using ICD-9 codes evaluated at emergency departments. HOUSES (HOUsing-based Index of SocioEconomic Status), a socioeconomic status measure based on individual housing features, was also calculated. Cox regression models were utilized to assess the association of the HOUSES (in quartiles) with accidental fall risk. RESULTS: Seven hundred eleven (5.8%) participants had at least one emergency room visit due to an accidental fall during the study period. Subjects with higher HOUSES were less likely to experience falls in a dose-response manner (hazard ratio: 0.58; 95% confidence interval: 0.44-0.76 for comparing the highest to the lowest quartile). In addition, the HOUSES was positively associated with better health behaviors, social support, and functional status. CONCLUSIONS: The HOUSES is inversely associated with accidental fall risk requiring emergency care in a dose-response manner. The HOUSES may capture falls-related risk factors through housing features and socioeconomic status-related psychosocial factors.

Liver transplantation after share 35: Impact on pretransplant and posttransplant costs and mortality.

Share 35 was implemented in 2013 to direct livers to the most urgent candidates by prioritizing Model for End-Stage Liver Disease (MELD) ≥ 35 patients. We aim to evaluate this policy's impact on costs and mortality. Our study includes 834 wait-listed patients and 338 patients who received deceased donor, solitary liver transplants at Mayo Clinic between January 2010 and December 2014. Of these patients, 101 (30%) underwent transplantation after Share 35. After Share 35, 29 (28.7%) MELD ≥ 35 patients received transplants, as opposed to 46 (19.4%) in the pre-Share 35 era (P = 0.06). No significant difference in 90-day wait-list mortality (P = 0.29) nor 365-day posttransplant mortality (P = 0.68) was found between patients transplanted before or after Share 35. Mean costs were $3,049 (P = 0.30), $5226 (P = 0.18), and $10,826 (P = 0.03) lower post-Share 35 for the 30-, 90-, and 365-day pretransplant periods, and mean costs were $5010 (P = 0.41) and $5859 (P = 0.57) higher, and $9145 (P = 0.54) lower post-Share 35 for the 30-, 90-, and 365-day posttransplant periods. In conclusion, the added cost of transplanting more MELD ≥ 35 patients may be offset by pretransplant care cost reduction. Despite shifting organs to critically ill patients, Share 35 has not impacted mortality significantly. Liver Transplantation 23:11-18 2017 AASLD.

A novel housing-based socioeconomic measure predicts hospitalisation and multiple chronic conditions in a community population.

BACKGROUND: Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population. METHODS: Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 2010, with follow-up until 31 December 2011. Primary outcome was all-cause hospitalisation over 1 calendar-year. Secondary outcome was MCC determined through a Minnesota Medical Tiering score. A logistic regression model was used to assess the association of HOUSES with the Minnesota tiering score. With adjustment for age, sex and MCC, the association of HOUSES with hospitalisation risk was tested using the Cox proportional hazards model. RESULTS: Eligible patients totalled 6402 persons (median age, 57 years; 25th-75th quartiles, 45-68 years). The lowest quartile of HOUSES was associated with a higher Minnesota tiering score after adjustment for age and sex (OR (95% CI) 2.4 (2.0 to 3.1)) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalisation (age, sex, MCC-adjusted HR (95% CI) 1.53 (1.18 to 1.98)) compared with those in the highest quartile. CONCLUSIONS: Low SES, as assessed by HOUSES, was associated with increased risk of hospitalisation and greater MCC health burden. HOUSES may be a clinically useful surrogate for SES to assess risk stratification for patient care and clinical research.