Wednesday, October 21, 2015: 10:00 AM - 11:30 AM
Grand Ballroom C (Hyatt Regency St. Louis at the Arch)

10:00 AM

Man Yee Mallory Leung, PhD, Washington University School of Medicine, St. Louis, MO, Graham Colditz, MD, DrPH, Washington University in St. Louis, Saint Louis, MO and Erika A. Waters, PhD, MPH, Washington University School of Medicine, Saint Louis, MO
Purpose: Racial disparities in health outcomes is a severe public health concern.  This study explores whether there are also racial disparities in medication expenditures for five major chronic diseases (diabetes, heart diseases, cancers, mental disorders, and chronic respiratory diseases) among non-institutionalized U.S. adults.

Method: We used data from the Medical Expenditure Panel Survey, 2011-2012, to study racial differences (white, black, Asian, Hispanic and other) in (1) annual medication expenditures, and (2) the number of visits that included medical prescriptions for major chronic diseases (prescription visits). The annual medication expenditures were estimated using two-part models, with logistic regression in the first part and generalized linear model with log-link and gamma variance in the second part. The number of prescription visits was estimated using zero-inflated Poisson model. Covariates included age, age squared, gender, logged total family income, education (<high school, high school diploma, college, graduate school), insurance status (private, Medicaid, Medicare, uninsured, and other public insurance), census region, and body mass index category. Complex sampling designs were adjusted for. All dollar values are expressed in 2012 US prices.

Result: Whites had significantly larger predicted medication expenditures and number of prescription visits than other races for most major chronic diseases in our study (all ps<.05). Across all five diseases, diabetes had the highest racial differentials in medication expenditures and number of prescription visits. Averaging over all non-white races, the medication expenditures differentials for non-whites compared to whites was -$1,279 for diabetes, -$812 for chronic respiratory diseases, -$466 for mental illness, -$444 for heart diseases, and -$433 for cancer. Compared to whites, non-whites had fewer prescription visits: -8.7 for diabetes, -6.3 for chronic respiratory diseases, -5.6 for mental illness, -4.7 for cancer, and -4.0 for heart diseases. Averaging over all five diseases, Asians had the largest differences in medical expenditures and number of visits with medical prescription with the whites, followed by blacks and Hispanics.

Conclusion: There are substantial racial differences in the predicted medication expenditures and number of visits with medical prescriptions for five major chronic diseases in the U.S., even controlling for key covariates. Future research should examine the causes of these disparities and the extent to which disparities in medical expenditures contribute to racial disparities in morbidity and mortality outcomes.

10:15 AM

Mary-Ellen Hogan, BScPhm, PharmD, MSc, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada, Vibhuti Shah, MD MSc, Institute of Health Policy, Management and Evaluation, University of Toronto, Department of Paediatrics, Mount Sinai Hospital, Toronto, ON, Canada, Joel Katz, BA, MA, PhD, Department of Psychology, York University, Toronto General Research Institute and Department of Anesthesia and Pain Management, University Health Network, Toronto, ON, Canada, Anna Taddio, BScPhm, MSc, PhD, Leslie Dan Faculty of Pharmacy, University of Toronto; Department of Child Health Evaluative Sciences, Pharmacy, Hospital for Sick Children, Toronto, ON, Canada and Murray D Krahn, MD, MSc, FRCPC, Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, ON, Canada
Purpose:      Approximately 19% of adults have chronic pain.  Technologies to treat chronic pain require cost estimates for cost-utility analyses.  In countries like Canada, with universal health coverage, costs borne by the government payer are of greatest interest to decision makers.    Methods:    Adults (18-64 years) with and without chronic pain were identified from the Canadian Community Health Survey (CCHS 2000-01, 2007-08, 2009-10).  Ontario respondents were linked to their administrative data which documents all publicly funded healthcare.  This includes hospital stays, emergency department use, physician visits, long-term and complex continuing care, homecare, rehabilitation and drugs for those ≥65 years or on social assistance.  Adults with chronic pain were matched to those without using age, sex, survey year, and a propensity score for having chronic pain; it was estimated from a rurality index, income quintile and comorbidity (ADGs, Johns Hopkins ACG system).  Per-person costs and use of healthcare were estimated for one year following survey response, adjusting for inflation.  Incremental cost was the difference in costs between individuals with pain and those without.   Results:    Chronic pain was reported by 13,129 (19%) of 67,619 CCHS respondents.  12,207 (93%) with chronic pain were matched to respondents without pain.  58% were female, mean age was 46 years (SD 12) and mean number of ADGs was 4.1 (SD 2.8) for each of cases and their matched controls.   One year healthcare utilization was greater in the chronic pain group versus the control group (p<0.01) for the following:  patients with at least 10 physician visits (56% vs 46%), at least 1 emergency department visit (32% vs 25%), at least 1 hospital stay (20% vs 16%), at least 1 CT (15% vs 7%), and at least 1 MRI (7% vs 4%).  Incremental costs were available for 8,298 pairs from CCHS 2007-08 and 2009-10, and are reported in the table.     Conclusions:    Adults with chronic pain used more services and costs were greater than matched controls without chronic pain.  The incremental cost is 30% more than what Ontario spends annually per capita ($3,800) on publicly funded healthcare. This study is the first to use Ontario administrative data to estimate healthcare costs for people with chronic pain.  The data will be useful for healthcare planning and will improve the quality of Canadian cost-utility analyses.

10:30 AM

Bradley Staats1, Hengchen Dai2, David Hofmann1 and Katherine L. Milkman, Ph.D.2, (1)UNC Kenan-Flagler Business School, Chapel Hill, NC, (2)The Wharton School, University of Pennsylvania, Philadelphia, PA
Purpose: One way to ensure greater compliance with organizational standards is by electronically monitoring employees’ activities. In the setting of hand hygiene in healthcare – a context where compliance is on average lower than 50% and where this lack of compliance can result in significant negative consequences – we investigated the effectiveness of electronic monitoring.

Method: We relied on data from a company that uses a radio frequency identification-based system to monitor healthcare workers’ hand hygiene compliance in hospitals. We observed over three-and-a-half years of compliance data from caregivers in 71 hospital units at 42 hospitals where electronic monitoring was deployed (encompassing over 20 million observations of hand hygiene opportunities). Since 71 hospital units activated electronic monitoring over the course of three years, this staggered roll-out allows us to isolate the effects of activating electronic monitoring on hand hygiene compliance from the effects of other potentially confounding factors, such as general time trends in hand hygiene compliance or the roll-out of a public campaign. The large number of hospital units involved in this study allows us to examine whether and why there is variability in the monitoring effect across hospital units. Also, the three-year longitudinal panel data allow us to explore whether the initial effects of activating electronic monitoring are strengthened over time or instead decay. For nine hospital units, electronic monitoring was discontinued, allowing us to evaluate the effects of the removal of monitoring on hand hygiene compliance. We used ordinary least squares (OLS) regressions to analyze our data.

Result: We find that, on average, caregivers exhibited a large and significant increase in hand hygiene compliance after electronic monitoring was activated. There is significant variability in the monitoring effect across units and that units with stronger social norms for hand hygiene compliance experienced bigger benefits from monitoring than units with weaker norms. Further, the benefits of monitoring increased for nearly two years before they eventually gradually degraded. Surprisingly, after monitoring was terminated, hand hygiene compliance did not sustain but dropped even below pre-monitoring levels.

Conclusion: Our findings highlight the need for not only implementing electronic monitoring, but also continuing to actively manage compliance efforts. The termination effect observed in our study highlights the limitations of merely monitoring desired behavior as a means of producing lasting compliance.

10:45 AM

Olga Kostopoulou, PhD1, Talya Porat, PhD1, Samhar Mahmood, PhD1, Derek Corrigan2 and Brendan C. Delaney, MD1, (1)King's College London, London, United Kingdom, (2)Royal College of Surgeons of Ireland, Dublin, Ireland
Purpose: To determine whether providing family physicians with a computerized diagnostic support system (DSS), integrated with the patient’s electronic health record (EHR), improves diagnostic accuracy. 

Method: A DSS prototype was designed and developed as part of the EU TRANSFoRm project (www.transformproject.eu). The prototype currently supports three reasons for encounter (RfE), abdominal pain, chest pain and dyspnoea, and is integrated with a commercial EHR system (InPS Vision3). It is triggered by the physician entering the RfE and immediately displays a list of suggested diagnoses for the specific patient, using also information extracted from the EHR. The principle of presenting family physicians with diagnoses to consider at the start of the encounter, before testing any diagnostic hypotheses, was shown to be effective with computer-simulated patients in two RCTs, in the UK and Greece (Kostopoulou and colleagues, 2015a & 2015b). In addition, the current prototype enables physicians easily to code both the presence and absence of symptoms and signs, while the list of suggested diagnoses is updated accordingly. At the end of the consultation, all the information that the physician has recorded is automatically transferred into the EHR.

In the evaluation study, 32 family physicians, users of the Vision3 EHR system, diagnosed 12 standardized patients (actors) in simulated clinics. Each physician first consulted with 6 patients using Vision3, and on a second occasion, with 6 different but matched for difficulty patients, using the DSS. The patient scenarios ranged in difficulty and were counterbalanced, so that they were all seen with and without the DSS across physicians. 

Result: Mean diagnostic accuracy was 0.50 [95% CI 0.42-0.58] without and 0.57 [0.50-0.64] with the DSS. Improvement in diagnostic accuracy was significant: odds ratio 1.33 [1.07-1.66] (P=0.01). The odds of giving a correct diagnosis doubled, on average, when scenario difficulty was accounted for in the regression model: OR 2.1 [1.43 to 2.83] (P<0.001).

Conclusion: This improvement in diagnostic accuracy is clinically significant, since the evaluation was done in a realistic environment, with actors as patients, and under the usual time pressures of the clinical consultation (10 minutes). Furthermore, this was the first time that the physicians were using the DSS prototype, following a 30-minute training session. Data analyses of the prototype’s usability and of patient satisfaction are on-going.

11:00 AM

Mette Holm Møller, Bsc Public Health, Aarhus University, Denmark, Aarhus, Denmark, Ivar Sønbø Kristiansen, MD, PhD, MPH, Department of Health Management and Health Economics, University of Oslo, Oslo, Norway, Christian Beisland, Department of Urology, Surgical Clinic, Haukeland University Hospital, Bergen, Norway, Jarle Rørvik, Department of Radiology, Haukeland University Hospital, Bergen, Norway and Henrik Støvring, Department of Public Health, Section for Biostatistics, Aarhus University, Aarhus, Denmark
Purpose: To estimate the changes in the stage distribution of prostate cancer (PC) after the introduction of opportunistic PSA-testing.

Method: From the Cancer Registry of Norway we obtained cancer stage, age and year of diagnosis on all men over the age of 50 diagnosed with PC during the period 1980-2010 in Norway. Three calendar-time periods were defined: One before the introduction of PSA-testing (1980-1989) and two after reflecting increasing diagnostic intensity (1990-2000 and 2001-2010); and three age groups: men eligible for PSA-testing (50-65 and 66-74) or older (75+). Birth-cohorts were categorized into four intervals: <1910, 1916-1925, 1926-1940 and >1941. We used Poisson regression to conduct a cross-sectional and a cohort-based analysis of trends in the incidence of localised, regional and distant cancer, respectively.

Result: The annual incidence of localised PC among men aged 50-65 and 66-74 rose from 41.4 and 255.2 per 100,000 before the introduction of PSA-testing to 137.9 and 418.7 in 2001-2010 afterwards, respectively, corresponding to 3.3 (CI: 3.1; 3.5) and 1.6 (CI: 1.6; 1.7) fold increases. The incidence of regional cancers increased by a factor seven and four among men aged <75 and 75+, respectively. The incidence of distant cancers among men aged 75+ decreased from 218.8 to 155.1 per 100,000, corresponding to a decrease of 0.7 (CI: 0.7; 0.8). The cohort-based analysis showed that the incidence of localised and regional PC shifted downwards to younger men, with a gradually decreased incidence of distant cancer in more recent cohorts.

Conclusion: Opportunistic PSA-testing substantially increased the incidence of localised and regional PC among men aged 50-74 years. The increase was not fully compensated in absolute numbers by the decrease in incidence of distant PC in older men, although it decreased by 30%.

11:15 AM

Ava John-Baptiste, PhD1, Taryn Becker, MD, MSc, FRCPC2, Hadas Fischer, MSc2, Kinwah Fung, MSc2, Lorraine Lipscombe, MD, MSc, FRCPC2, Peter C. Austin, PhD3 and Geoffrey Anderson, MD, MSC, PhD4, (1)Western University, London, ON, Canada, (2)Women's College Research Institute, Toronto, ON, Canada, (3)Institute for Clinical Evaluative Sciences, Toronto, ON, Canada, (4)University of Toronto, Toronto, ON, Canada
Purpose: Randomized controlled trials (RCTs) provide information on drug efficacy prior to licensing. Administrative databases serve important roles in identifying and monitoring adverse event risks post-market. For postmenopausal women with early breast cancer, aromatase inhibitors (anastrazole, letrozole or exemestane) reduce the risk of breast cancer recurrence compared to Tamoxifen, but may increase fracture risk. We incorporated prior information on the risk of hip, spine and wrist fracture derived from pre-market RCTs into post-market analyses using administrative data. 

Method: We conducted a retrospective cohort study of women age 66 or older diagnosed with early breast cancer, in Ontario, Canada from 2003 to 2010, who initiated treatment with an aromatase inhibitor (AI) or Tamoxifen (Tam). Prior information from meta-analyses of pre-market RCTs indicated an increased risk of hip, spine and wrist fracture with a probability that the relative risk (RR) exceeded 1 of 92% (RR=1.73, 95% Credible Interval (CrI) 0.81, 3.68), 98% (RR= 1.46, 95%CrI 1.02, 2.09) and 94% (RR= 1.48, 95%CrI 0.9, 2.44), respectively. Using Cox proportional hazard regression, we modeled the hazard of fracture occurrence, estimating the relative hazard ratio (HR) for AI compared to Tam, adjusting for age, fracture history, corticosteroid use, rheumatoid arthritis, dementia and diabetes. Using data augmentation, we incorporated both uninformative and informative priors into the analyses. We estimated posterior probabilities that the HR exceeded 1, assuming a normal distribution for regression coefficients.

Result: A smaller proportion of women who initiated treatment on AI (n=6,526) experienced fractures (hip: 2.4%, spine: 1.7%, wrist: 3.8%) compared to women initiated on Tam (n=3,733, hip: 3.8%, spine: 2.3%, wrist: 4.1%) but the mean time to fracture was shorter on AI. After risk adjustment, posterior probabilities that the HR exceeded 1 for AI compared to Tam were 46% (HR=0.99, 95%CrI 0.71, 1.25), 35% (HR=0.94, 95%CrI 0.78, 1.26) and 76% (HR=1.08, 95%CrI 0.88, 1.32) with an uninformative prior, and 63% (HR=1.04, 95%CrI 0.83, 1.3), 84% (HR=1.12, 95%CrI 0.89, 1.4) and 89% (HR=1.13, 95%CrI 0.93, 1.36) with an informative prior, for hip, spine and wrist fracture, respectively.

Conclusion: In our study, informative prior information derived from RCTs increased posterior probabilities of greater fracture risk for AI compared to Tam. This approach incorporated safety data from a range of sources and can be implemented in commonly used statistical software.