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Few studies have quantified the multimorbidity burden in older adults with chronic obstructive pulmonary disease (COPD) using large and generalizable data. Such evidence is essential to inform evidence-based research, clinical care, and resource allocation. This retrospective cohort study used a nationally representative sample of Medicare beneficiaries aged 65 years or older with COPD and 1:1 matched (on age, sex, and race) non-COPD beneficiaries to: (1) quantify the prevalence of multimorbidity at COPD onset and one-year later; (2) quantify the rates [per 100 person-years (PY)] of newly diagnosed multimorbidity during in the year prior to and in the year following COPD onset; and (3) compare multimorbidity prevalence in beneficiaries with and without COPD. Among 739,118 eligible beneficiaries with and without COPD, the average number of multimorbidity was 10.0 (SD = 4.7) and 1.0 (SD = 3.3), respectively. The most prevalent multimorbidity at COPD onset and at one-year after, respectively, were hypertension (70.8% and 80.2%), hyperlipidemia (52.2% and 64.8%), anemia (42.1% and 52.0%), arthritis (39.8% and 47.7%), and congestive heart failure (CHF) (31.3% and 38.8%). Conditions with the highest newly diagnosed rates before and following COPD onset, respectively, included hypertension (39.8 and 32.3 per 100 PY), hyperlipidemia (22.8 and 27.6), anemia (17.8 and 20.3), CHF (16.2 and 13.2), and arthritis (12.9 and 13.2). COPD was significantly associated with increased odds of all measured conditions relative to non-COPD controls. This study updates existing literature with more current, generalizable findings of the substantial multimorbidity burden in medically complex older adults with COPD-necessary to inform patient-centered, multidimensional care.Supplemental data for this article is available online at https://doi.org/10.1080/15412555.2021.1968815 .

Author(s): Ren, Ai; Golden, Bruce; Alt, Frank; Wasil, Edward; Bjarnadottir, Margret; Hirshon, Jon Mark; Pimentel, Laura | Abstract: Introduction: On January 1, 2014, the State of Maryland implemented the Global Budget Revenue (GBR) program. We investigate the impact of GBR on length of stay (LOS) for inpatients in emergency departments (ED) in Maryland.Methods: We used the Hospital Compare data reports from the Centers for Medicare and Medicaid Services (CMS) and CMS Cost Reports Hospital Form 2552-10 from January 1, 2012–March 31, 2016, with GBR hospitals from Maryland and hospitals from West Virginia (WV), Delaware (DE), and Rhode Island (RI). We implemented difference-in-differences analysis and investigated the impact of GBR implementation on the LOS or ED1b scores of Maryland hospitals using a mixed-effects model with a state-level fixed effect, a hospital-level random effect, and state-level heterogeneity.Results: The GBR impact estimator was 9.47 (95% confidence interval [CI], 7.06 to 11.87, p-valuel0.001) for Maryland GBR hospitals, which implies, on average, that GBR implementation added 9.47 minutes per year to the time that hospital inpatients spent in the ED in the first two years after GBR implementation. The effect of the total number of hospital beds was 0.21 (95% CI, 0.089 to 0.330, p-value = 0 .001), which suggests that the bigger the hospital, the longer the ED1b score. The state-level fixed effects for WV were -106.96 (95% CI, -175.06 to -38.86, p-value = 0.002), for DE it was 6.51 (95% CI, -8.80 to 21.82, p-value=0.405), and for RI it was -54.48 (95% CI, -82.85 to -26.10, p-valuel0.001).Conclusion: Our results indicate that GBR implementation has had a statistically significant negative impact on the efficiency measure ED1b of Maryland hospital EDs from January 2014 to April 2016. We also found that the significant state-level fixed effect implies that the same inpatient might experience different ED processing times in each of the four states that we studied. [West J Emerg Med.

BACKGROUND CONTEXT Nonspecific neck and back pain are highly variable conditions with hundreds of treatment options. Using standard analytical techniques, it is difficult to understand how patients seek and receive health care across service locations. PURPOSE To build and display a comprehensive understanding of common utilization patterns for nonspecific neck and back pain. Also, to understand how observable patient characteristics (including insurance type, age and comorbidities) and initial sites of care (patient self-triage decisions to one of 9 initial sites of care including primary care, chiropractor and the emergency room) are associated with subsequent treatment and utilization patterns. STUDY DESIGN/SETTING This retrospective, observational study leveraged a US multipayer medical and pharmaceutical administrative claims dataset. Although not nationally representative, the data are comprised of commercial and Medicaid insured patients from 47 states and the District of Columbia. Each patient was eligible for at least 24 months in the database, enabling researchers to study utilization patterns over time and across service locations. PATIENT SAMPLE We identified a sample of 113,654 episodes for patients aged 18-64 who received care for nonspecific neck or back pain. Patients had a neck and back pain “clean” period of at least 12 months prior to initial presentation and were observed 6 months after the index claim. Subjects were excluded if their medical history and pain causes indicated potential deviation from conservative diagnostic and treatment practices. OUTCOME MEASURES The patient's initial site of care, the patient's subsequent diagnostic and treatment pattern (one of 14 clusters representing the 6 months after the index neck or back pain event), and common utilization metrics (eg, procedures, emergency care, and opioid prescription rates) within 6 months of the initial encounter. METHODS We applied a sequence alignment and density-based clustering methodology to identify treatment pathways based on the types and ordering of each patient's neck and back pain-related events for 6 months after the index claim. Using a series of multinomial models, we examined how observable patient characteristics were associated with the initial site of care and how the initial site of care was associated with the patient's subsequent treatment pathway. Prediction accuracy was evaluated using an 80% training, 20% testing division of the sample. We calculated health care utilization during the first 6 months after the index event. RESULTS The clustering methodology discovered 14 distinct patterns of how patients are diagnosed and treated for nonspecific neck and back pain in the first 6 months after the index encounter. The patterns ranged significantly in terms of utilization rates of advanced diagnostic imaging, opioid prescribing, and use of conservative therapy. The algorithm also identified 3.1% of episodes in which patients underwent uncommon treatment plans that included invasive procedures, multiple advanced images, and emergency or inpatient admissions. While some patient characteristics (including insurance type and geography) were associated with the patient's initial site of care, patient characteristics alone could not accurately predict this self-triage decision for an individual patient (47.9% vs 45.2% naive prediction). In contrast, the initial site of care is highly predictive of the subsequent treatment pathway (42.3% prediction accuracy using initial site of care alone vs 30.3% naive prediction). CONCLUSIONS The patient journey clustering methodology created a granular, data-driven understanding into how patients are diagnosed and treated after an index neck or back pain claim. The analyses suggest that the patient's self-triage decision to an initial site of care is difficult to predict using observable patient characteristics. However, once this initial site of care is chosen, the patient's subsequent treatment pattern and utilization within the first 6 months becomes more predictable. Follow-up studies on more generalizable samples are needed to use the data to proactively route patients toward high-quality, appropriate treatment plans. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

We present a theory of how a rational, profit-maximizing firm would respond to pressure for gender pay equity by strategically distributing raises to reduce the pay gap between its female and male employees at minimum cost. Using formal analysis and pay data from a real employer, we show that (1) employees in low-paying jobs and whose pay-related observables are similar to those of men at the firm are most likely to get raises; (2) counterintuitively, some men may get raises, and giving raises to certain women would increase the pay gap; and (3) a firm can reduce the gender pay gap as measured by a much larger percentage than the overall increase in pay to women at the firm. We also identify the conditions under which a firm could “explain away” a gender pay gap using other pay-related observables, such as job category, as well as the conditions under which this strategy would backfire. Our paper helps explain some empirical puzzles, such as the tendency for some men to get raises after gender equity pay reviews, and yields a rich set of implications for empirical research and practice. The online appendix is available at https://doi.org/10.1287/orsc.2018.1248 .

Having accurate, unbiased prognosis information can help patients and providers make better decisions about what course of treatment to take. Using a comprehensive dataset of all colorectal cancer patients in California, we generate predictive models that estimate short‐term and medium‐term survival probabilities for patients based on their clinical and demographic information. Our study addresses some of the contradictions in the literature about survival rates and significantly improves predictive power over the performance of any model in previously published studies.

We explore whether free agents in Major League Baseball meet the expectations set forth by newly signed contracts. The value and duration of these contracts are negotiated between the player and his agent and the signing team and are based primarily on the player's performance to date, projected future performance, and potential marketing value to the team. We develop two classes of models to explore this problem using a variety of regression- and tree-based machine learning algorithms. The market model uses player and team data to predict the market value of a player's performance i.e., average contract salary. The performance model uses the same data to predict wins above replacement as a surrogate for overall player performance. We translate this measure into dollars using position-based conversion factors. Analysis of these models demonstrates that the performance model more consistently predicts and assesses player value with respect to their free agent contracts. Together, these models can be used to target or avoid free agents or other players whose performance-based value differs significantly from their market value. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

Background: Optimization of health care delivery in different settings with the goal of improving quality of life and reduction of heart failure (HF) morbidity continues to be an unmet need. Digital engagement between patients and providers is becoming more prevalent for care coordination and management of heart failure.Aims: To design and test a customized and interactive heart failure disease specific functionality with activated technology (Amazon's Echo Dot or an Avatar) as a tool for management of HF patients at home. To identify the effect of a voice activated technology in the reduction of heart failure hospitalizations, improvement of medication adherence, improvement in health-related quality of life and increase in comfort with voice activated technologies. Methods We compared two types of conversational agent technologies to standard of care, using speech recognition, to evaluate patient engagement in HF patients. The first technologyutilized the Amazon Echo Dot Alexa technology and only communicated with the patient through audio and the second was an application on a tablet using an Avatar, combining audio and visual. The conversational agent asks the HF patients a series of 11 questions, divided into three components: compliance (questions 1-3), mild HF symptoms (questions 4-6) and moderate/severe HF symptoms (questions 7-11). Participants completed questions regarding their use and comfort with technology. Inclusion criteria: admitted or treated for HF;18 or older; and resides with access to wifi. Exclusion criteria: heart transplant or ventricular assist device recipients. Participants were monitored for three months.Results: Thirty patients were enrolled in each intervention arm of the two studies with the Alexa or Avatar. Mean age was 54 years in the Alexa group and 56 years in the Avatar group. There were predominately males 61% and 63%, Alexa and Avatar, respectively. In the Alexa and Avatar groups race was predominately black, 64% and 63% followed by white 21% and 30%. The Alexa group was statistically more likely to complete the three compliance factors of weight checking (p=.030), salt monitoring (p=0.016) and medication adherence (p=0.005). Initial results suggest that older people and individuals using the smart phone answer the questionnaire at higher rates, and individuals taking an increased number of medications to treat HF had lower participation.Conclusion: Voice enabled, internet connected devices are poised to have profound impacts on the quality of life for in-home, independent, and assisted-living patients by vastly increasing the connectivity to healthcare providers.

Case management is a $6 billion industry that employs over 34,000 people in the United States alone. Traditionally, case management has been utilized to help patients navigate the health care system and to coordinate care in the hope of lowering costs and achieving better health outcomes. However, since enrollment into these programs is typically either universal or limited to very sick patients, studies on the cost-effectiveness of case management programs find that their performance is mixed, at best. In this article we posit an opportunity to improve outcomes and lower costs by targeting certain patients for case management and early intervention. Utilizing modern data mining methods, we develop a methodology to identify these patients, who we describe as “jumpers” because their costs are currently low but will increase significantly in the near future. Given the performance of the prediction models, we also show that unless case management can prevent over 7.5% of health care cost increases, i...

Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical practitioners, but with the widespread adoption of electronic medical records and comprehensive patient databases, surveillance systems that utilize such data are of considerable interest. Unfortunately, existing methods for analyzing the data in such systems ignore the open-ended exploratory nature of such systems that requires the assessment of multiple possible adverse events. In this article, we propose a method, SEQMEDS, that assesses the effect of a single drug on multiple adverse events by analyzing data that accumulate sequentially and explicitly captures interdependencies among the multiple events. The method continuously monitors a vector-valued test-statistic derived from the cumulative number of adverse events. It flags a potential adverse event once the test-statistic crosses a stopping boundary. We employ asymptotic analysis that assumes a large number of observations in a given window of time to show how to compute the stopping boundary by solving a convex optimization problem that achieves a desired Type I error and minimizes the expected time to detection under a pre-specified alternative hypothesis. We apply our method to a model in which the interdependency among the multiple adverse events is captured by a Cox proportional hazards model with time-dependent covariates and demonstrate that it provides an approximation of a fully sequential test for the maximum hazard ratio of the drug over multiple adverse events. A numerical study verifies that our method delivers Type I /II errors that are below pre-specified levels and is robust to distributional assumptions and parameter values.

The rising cost of health care is one of the world's most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data-mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 200,000 members. The key findings are: (a) our data-mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health-care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high-cost members.