Mild traumatic brain injury in the United States: demographics, brain imaging procedures, health-care utilization and costs.

Objective: To characterize mild traumatic brain injury (mTBI) patients in the USA, describing location of diagnosis, timing, and modality of imaging procedures, health-care resource utilization (HRU) and costs in the 12-month period post-diagnosis. Research Design: Retrospective claims analysis Methods: Anonymized data from the OptumHealth Care Solutions claims database (2006-2016). The index date was the first date with an mTBI diagnosis. HRU and costs (2016 USD) were assessed in the 12-month post-index period. Results: A total of 80,004 patients with mTBI were included: 60% were under 26 years and 54% were male. Mild TBI was most frequently diagnosed in an emergency department (ED) for all age groups, except patients aged 11-17 years, for whom the outpatient setting was the most frequent place of diagnosis. Almost half (47%) received brain imaging on the index date, with 98% of which receiving computed tomography. Mean follow-up health-care costs were $13,564 (SD = $41,071), primarily from inpatient ($4,675, SD = $29,982) and non-ED outpatient/physician office visits ($4,207, SD = $12,697). Older patients had greater HRU and higher health-care costs. Conclusions: The findings of this claims-based study show substantial HRU and costs associated with mTBI diagnosis during a 12-month follow-up period.

Economic and clinical benefits of early identification of acute kidney injury using a urinary biomarker.

Purpose: To evaluate the budget impact of adding a diagnostic test of tissue inhibitor of metalloproteinase 2 and insulin-like growth factor binding protein 7 ([TIMP-2]·[IGFBP7]), which identifies patients at risk of moderate-to-severe acute kidney injury (AKI), to the current standard of care (SOC) in a hospital setting.Materials and methods: A budget impact model (2017 USD) was developed from the perspective of a hypothetical US hospital system serving 10,000 inpatients annually. The model estimated the impact of assessing the risk of AKI using SOC vs a combination of SOC and the US Food and Drug Administration-approved assay [TIMP-2]·[IGFBP7] over a 1-year period. Potential cost implications were assessed using estimates for payer mix among patients, diagnostic efficacy, and patient healthcare resource utilization. The model also considered provider adoption rates and the estimated costs of [TIMP-2]·[IGFBP7].Results: Compared to SOC alone, adding [TIMP-2]·[IGFBP7] to SOC was associated with a $1,855 reduction in uncompensated care per patient tested, which, after accounting for the additional costs of the test ($277), resulted in net savings of $1,578 per patient tested. The findings were robust to input parameter variations, as demonstrated by deterministic and probabilistic sensitivity analyses. In the probabilistic sensitivity analyses, net cost savings to the hospital ranged from $50,308-$3,971,514, or $101-$7,943 per tested patient (mean = $1,710; 95% confidence interval = $1,691-$1,729).Conclusions: The introduction of [TIMP-2]·[IGFBP7] as a novel tool in the identification of AKI risk may result in considerable cost savings from a hospital perspective under this model's base-case assumptions. Further prospective studies are needed to confirm these findings in a real-world setting.Key points for decision makersAn economic model was constructed to determine the budget impact of adding a diagnostic test ([TIMP-2]·[IGFBP7]), which identifies patients at risk of moderate-to-severe acute kidney injury, to the current standard of care (SOC) in a hospital setting.According to the present model, the use of [TIMP-2]·[IGFBP7] to identify acute kidney injury risk may reduce costs for hospitals by ∼$1,578 per patient tested.

Trends in health service use and potentially avoidable hospitalizations before Alzheimer's disease diagnosis: A matched, retrospective study of US Medicare beneficiaries.

INTRODUCTION: This study evaluates rates of all-cause emergency department visits, all-cause hospitalizations, potentially avoidable hospitalizations, and falls in 3 years preceding Alzheimer's disease (AD) diagnosis. METHODS: Patients with AD and controls with no cognitive impairment were identified from the Medicare claims data. Patients were required to be aged ≥ 65 years and have continuous Medicare enrollment for ≥4 years before the index date (AD cohort: first AD diagnosis in 2012-2014; controls: randomly selected medical claim). Outcomes for each preindex year were compared among propensity score-matched cohorts. RESULTS: Each year, before index, patients with AD were more likely to have all-cause emergency department visits, all-cause hospitalizations, potentially avoidable hospitalizations, and falls (P < .05 for all comparisons) than matched controls (N = 19,679 pairs). Increasing absolute and relative risks over time were observed for all outcomes. DISCUSSION: The study findings highlight the growing burden of illness before AD diagnosis and emphasize the need for timely recognition and management of patients with AD.

Reliability of coded data to identify earliest indications of cognitive decline, cognitive evaluation and Alzheimer's disease diagnosis: a pilot study in England.

OBJECTIVES: Evaluate the reliability of using diagnosis codes and prescription data to identify the timing of symptomatic onset, cognitive assessment and diagnosis of Alzheimer's disease (AD) among patients diagnosed with AD. METHODS: This was a retrospective cohort study using the UK Clinical Practice Research Datalink (CPRD). The study cohort consisted of a random sample of 50 patients with first AD diagnosis in 2010-2013. Additionally, patients were required to have a valid text-field code and a hospital episode or a referral in the 3 years before the first AD diagnosis. The earliest indications of cognitive impairment, cognitive assessment and AD diagnosis were identified using two approaches: (1) using an algorithm based on diagnostic codes and prescription drug information and (2) using information compiled from manual review of both text-based and coded data. The reliability of the code-based algorithm for identifying the earliest dates of the three measures described earlier was evaluated relative to the comprehensive second approach. Additionally, common cognitive assessments (with and without results) were described for both approaches. RESULTS: The two approaches identified the same first dates of cognitive symptoms in 33 (66%) of the 50 patients, first cognitive assessment in 29 (58%) patients and first AD diagnosis in 43 (86%) patients. Allowing for the dates from the two approaches to be within 30 days, the code-based algorithm's success rates increased to 74%, 70% and 94%, respectively. Mini-Mental State Examination was the most commonly observed cognitive assessment in both approaches; however, of the 53 tests performed, only 19 results were observed in the coded data. CONCLUSIONS: The code-based algorithm shows promise for identifying the first AD diagnosis. However, the reliability of using coded data to identify earliest indications of cognitive impairment and cognitive assessments is questionable. Additionally, CPRD is not a recommended data source to identify results of cognitive assessments.

Opioid Abuse: A Detailed Examination of Cost Drivers over a 24-Month Follow-up Period.

BACKGROUND: Previous work has documented the considerable economic burden associated with opioid abuse, dependence, and overdose/poisoning (hereafter, "abuse"). Recent analyses have provided insights into the trajectory and drivers of the excess costs of abuse both before and after diagnosis, showing the important role of other substance abuse, mental health issues, and painful conditions. OBJECTIVE: To build on the recently published study by Kirson et al. (2017) and extend its findings by (a) evaluating the trajectory of excess costs of abuse for an additional year after an incident abuse diagnosis and (b) exploring the diagnosis-level drivers of excess costs over time in greater detail. METHODS: Using administrative medical and pharmacy claims, which included payment amounts, for beneficiaries covered by large self-insured companies throughout the United States, abusers were matched to controls using the same methods as in Kirson et al. Excess health care costs were assessed over a 24-month follow-up period, which comprised the 6 months before the initial abuse diagnosis and the 18 months after. Drivers of excess costs were then evaluated by diagnosis (grouped at the 3-digit ICD-9-CM level). RESULTS: This study analyzed 9,345 matched pairs of abusers and non-abusers. Similar to the previous study, mean per-patient excess health care costs were found to rise considerably leading up to and shortly after the incident diagnosis of abuse, reaching $15,764 over the first half of the follow-up period. Over the newly extended follow-up period (months 6 to 18 after diagnosis), excess costs remained elevated ($7,346) and did not return to baseline levels. Over time, an increasing share of excess costs was observed for outpatient services and prescription drug use, relative to acute care settings. A detailed examination of cost drivers suggested elevated costs in several clinical categories (e.g., gastrointestinal, respiratory conditions) beyond those previously identified. CONCLUSIONS: This research finds that the excess medical costs of abuse extend for at least 1 more year than previously documented, reflecting the need for considerable follow-up care over time. The identification of several other clinical categories with elevated excess costs suggests important areas for future research into the interaction of opioid abuse with the management of other conditions. DISCLOSURES: This study was funded by Purdue Pharma. Howard was an employee of Purdue Pharma at the time that this study was conducted. Kirson, Scarpati, Jia, and Wen are employees of Analysis Group, which received funding from Purdue Pharma to conduct this study. Study concept and design were contributed by Kirson, Scarpati, and Howard, along with Jia and Wen. Jia and Wen took the lead in data collection, with assistance from Scarpati and Kirson. Data interpretation was performed by Scarpati, Kirson, and Howard, with assistance from Jia and Wen. The manuscript was written and revised by Scarpati, Kirson, and Howard.