Costs Attributable to Clostridioides difficile Infection Based on the Setting of Onset.

BACKGROUND: Although hospital-onset Clostridioides difficile infection (CDI) is associated with significant healthcare costs, the economic burden of CDI with onset in other facilities or the community has not been well studied. METHODS: Incident CDI cases were identified using 2011-2017 Medicare fee-for-service data. Controls were randomly selected in a 4:1 ratio matching to the CDI case surveillance definition. Inverse probability of exposure weights were used to balance on measured confounders. One-, 3-, and 5-year cumulative costs attributable to CDI were computed using a 3-part estimator (parametric survival model and pair of 2-part models predicting costs separately in intervals where death did and did not occur). RESULTS: A total of 60 492 CDI cases were frequency-matched to 241 968 controls. One-, 3-, and 5-year adjusted attributable costs were highest for hospital-onset CDI at $14 257, $18 953, and $21 792, respectively, compared with hospitalized controls and lowest for community-associated CDI compared with community controls at $1013, $3161, and $6454, respectively. Adjusted 1-, 3-, and 5-year costs attributable to community-onset healthcare facility-associated CDI were $8222, $13 066, and $16 329 and for other healthcare facility-onset CDI were $5345, $6764, and $7125, respectively. CONCLUSIONS: Economic costs attributable to CDI in elderly persons were highest for hospital-onset and community-onset healthcare facility-associated CDI. Although lower, attributable costs due to CDI were significantly higher in cases with CDI onset in the community or other healthcare facility than for comparable persons without CDI. Additional strategies to prevent CDI in the elderly are needed to reduce morbidity and healthcare expenditures.

Autoimmune conditions and pancreatic cancer risk in older American adults.

Pancreatic cancer (PC) is highly fatal, and its incidence is increasing in the United States. Population-based registry studies suggest associations between a few autoimmune conditions and PC risk, albeit based on a relatively small number of cases. We conducted a population-based, nested case-control study to examine the associations between autoimmune conditions and PC risk within the Surveillance, Epidemiology, and End Results Program (SEER)-Medicare population. Incident primary malignant PC cases (n = 80 074) were adults ≥66 years and diagnosed between 1992 and 2015. Controls (n = 320 296) were alive at the time cases were diagnosed and frequency-matched to cases (4:1 ratio) by age, sex, and year of diagnosis. We used multivariable-adjusted, unconditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for 45 autoimmune conditions identified from Medicare claims. Eight autoimmune conditions including ankylosing spondylitis (OR = 1.45; 95% CI: 1.14-1.84), Graves' disease (OR = 1.18; 95% CI: 1.03-1.34), localized scleroderma (OR = 1.27; 95% CI: 1.06-1.52), pernicious anemia (OR = 1.08; 95% CI: 1.02-1.14), primary sclerosing cholangitis (OR = 1.37; 95% CI: 1.18-1.59), pure red cell aplasia (OR = 1.31; 95% CI: 1.16-1.47), type 1 diabetes (OR = 1.11; 95% CI: 1.07-1.15), and ulcerative colitis (OR = 1.18; 95% CI: 1.07-1.31) were associated with increased PC risk (false discovery rate-adjusted P values <.10). In subtype analyses, these conditions were associated with pancreatic ductal adenocarcinoma, whereas only ulcerative colitis was associated with pancreatic neuroendocrine tumors. Our results support the hypothesis that autoimmune conditions may play a role in PC development.

Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data.

Clinical treatment outcomes are the quality and cost targets that health-care providers aim to improve. Most existing outcome analysis focuses on a single disease or all diseases combined. Motivated by the success of molecular and phenotypic human disease networks (HDNs), this article develops a clinical treatment network that describes the interconnections among diseases in terms of inpatient length of stay (LOS) and readmission. Here one node represents one disease, and two nodes are linked with an edge if their LOS and number of readmissions are conditionally dependent. This is the very first HDN that jointly analyzes multiple clinical treatment outcomes at the pan-disease level. To accommodate the unique data characteristics, we propose a modeling approach based on two-part generalized linear models and estimation based on penalized integrative analysis. Analysis is conducted on the Medicare inpatient data of 100,000 randomly selected subjects for the period of January 2010 to December 2018. The resulted network has 1008 edges for 106 nodes. We analyze key network properties including connectivity, module/hub, and temporal variation. The findings are biomedically sensible. For example, high connectivity and hub conditions, such as disorders of lipid metabolism and essential hypertension, are identified. There are also findings that are less/not investigated in the literature. Overall, this study can provide additional insight into diseases' properties and their interconnections and assist more efficient disease management and health-care resources allocation.

Explanations for variations in hospital expenditures among four large California counties.

OBJECTIVE: To investigate competing explanations for why Medicare Fee for Service (FFS) and private sector payments lead to hospital cost variations in Californian counties. DATA SOURCES: Ratios of private to Medicare hospital costs were obtained from state-based all-payer claims databases. Demographics were estimated from the U.S. Census Bureau and the California Health Interview Survey. Medicaid and Medicare spending was obtained from Kaiser Family Foundation. Medicare Advantage enrollment was obtained from the California Department of Health Care Services and market consolidation was estimated using the Herfindahl-Hirschman Index (HHI). STUDY DESIGN: Per capita costs, demographics, Medicaid and Medicare spending, Medicare Advantage enrollment, and HHI scores were compared for San Francisco (SF), Sacramento, Los Angeles (LA), and San Diego (SD). PRINCIPAL FINDINGS: LA hospitals had the lowest per capita private insurer costs, but the highest Medicare FFS costs. The findings might be explained by a lower HHI for LA, indicating a more competitive market, than SD, SF, and Sacramento. CONCLUSIONS: Medicare FFS hospital costs do not provide an accurate representation of health care spending in Californian counties. In more competitive markets, private insurance companies can negotiate lower prices, while oversupply may allow facilities to increase volume in Medicare FFS.

Do functional status and Medicare claims data improve the predictive accuracy of an electronic health record mortality index? Findings from a national Veterans Affairs cohort.

BACKGROUND: Electronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index. METHODS: This was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots. RESULTS: In the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration. CONCLUSIONS: Incorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

Human disease clinical treatment network for the elderly: The analysis of medicare inpatient length of stay data.

Disease clinical treatment measures, such as inpatient length of stay (LOS), have been examined for most if not all diseases. Such analysis has important implications for the management and planning of health care, financial, and human resources. In addition, clinical treatment measures can also informatively reflect intrinsic disease properties such as severity. The existing studies mostly focus on either a single disease (or a few pre-selected and closely related diseases) or all diseases combined. In this study, we take a new and innovative perspective, examine the interconnections in length of stay (LOS) among diseases, and construct the very first disease clinical treatment network on LOS. To accommodate uniquely challenging data distributions, a new conditional network construction approach is developed. Based on the constructed network, the analysis of important network properties is conducted. The Medicare data on 100 000 randomly selected subjects for the period of January 2008 to December 2018 is analyzed. The network structure and key properties are found to have sensible biomedical interpretations. Being the very first of its kind, this study can be informative to disease clinical management, advance our understanding of disease interconnections, and foster complex network analysis.

Evaluation of Survival Outcomes of Endovascular Versus Open Aortic Repair for Abdominal Aortic Aneurysms with a Big Data Approach.

Abdominal aortic aneurysm (AAA) is a localized enlargement of the abdominal aorta. Once ruptured AAA (rAAA) happens, repairing procedures need to be applied immediately, for which there are two main options: open aortic repair (OAR) and endovascular aortic repair (EVAR). It is of great clinical significance to objectively compare the survival outcomes of OAR versus EVAR using randomized clinical trials; however, this has serious feasibility issues. In this study, with the Medicare data, we conduct an emulation analysis and explicitly "assemble" a clinical trial with rigorously defined inclusion/exclusion criteria. A total of 7826 patients are "recruited", with 3866 and 3960 in the OAR and EVAR arms, respectively. Mimicking but significantly advancing from the regression-based literature, we adopt a deep learning-based analysis strategy, which consists of a propensity score step, a weighted survival analysis step, and a bootstrap step. The key finding is that for both short- and long-term mortality, EVAR has survival advantages. This study delivers a new big data strategy for addressing critical clinical problems and provides valuable insights into treating rAAA using OAR and EVAR.

Progression of diabetes, ischemic heart disease, and chronic kidney disease in a three chronic conditions multistate model.

BACKGROUND: Diabetes mellitus, ischemic heart disease, and chronic kidney disease are three major chronic conditions that develop with increasing risks among adults as they get older. The interconnectedness of these three chronic conditions is well known, while each condition acts as a prognostic risk factor for the other two. It is important to understand the progressive relationships of these three conditions over time in terms of transitioning between clinical states and the impact on patients' survival. METHODS: We investigate the survival characteristics of a Medicare population aged 65 years and above in a multistate system that contained clinical states specified by death and diagnosis combinations of three chronic conditions. The study was conducted using Hawaii Medicare claims data from 2009 to 2013. To evaluate the progression of a subject with one of the newly diagnosed chronic conditions, we analyzed quantities such as state occupation probabilities in eight states and hazards of sixteen transition types. We quantified effects and significances of potential covariates such as age, gender, race/ethnicity, comorbidity burden and financial status on these temporal functions. Nonparametric method of estimating state occupation probabilities and pseudo-value based method for estimating covariate effects of a survival system were utilized. RESULTS: We found a range of age, gender, race/ethnicity and financial status based interesting covariate influences on transitions and state occupation probabilities of the system. CONCLUSION: Survival characteristics of the disease system are influenced by subject-specific effects. Subgroup-specific interventions/screenings should be considered for the optimal prevention and care.

The burden of clostridium difficile infection: estimates of the incidence of CDI from U.S. Administrative databases.

BACKGROUND: Many administrative data sources are available to study the epidemiology of infectious diseases, including Clostridium difficile infection (CDI), but few publications have compared CDI event rates across databases using similar methodology. We used comparable methods with multiple administrative databases to compare the incidence of CDI in older and younger persons in the United States. METHODS: We performed a retrospective study using three longitudinal data sources (Medicare, OptumInsight LabRx, and Healthcare Cost and Utilization Project State Inpatient Database (SID)), and two hospital encounter-level data sources (Nationwide Inpatient Sample (NIS) and Premier Perspective database) to identify CDI in adults aged 18 and older with calculation of CDI incidence rates/100,000 person-years of observation (pyo) and CDI categorization (onset and association). RESULTS: The incidence of CDI ranged from 66/100,000 in persons under 65 years (LabRx), 383/100,000 in elderly persons (SID), and 677/100,000 in elderly persons (Medicare). Ninety percent of CDI episodes in the LabRx population were characterized as community-onset compared to 41 % in the Medicare population. The majority of CDI episodes in the Medicare and LabRx databases were identified based on only a CDI diagnosis, whereas almost ¾ of encounters coded for CDI in the Premier hospital data were confirmed with a positive test result plus treatment with metronidazole or oral vancomycin. Using only the Medicare inpatient data to calculate encounter-level CDI events resulted in 553 CDI events/100,000 persons, virtually the same as the encounter proportion calculated using the NIS (544/100,000 persons). CONCLUSIONS: We found that the incidence of CDI was 35 % higher in the Medicare data and fewer episodes were attributed to hospital acquisition when all medical claims were used to identify CDI, compared to only inpatient data lacking information on diagnosis and treatment in the outpatient setting. The incidence of CDI was 10-fold lower and the proportion of community-onset CDI was much higher in the privately insured younger LabRx population compared to the elderly Medicare population. The methods we developed to identify incident CDI can be used by other investigators to study the incidence of other infectious diseases and adverse events using large generalizable administrative datasets.

Targeting Community-Based Palliative and Serious Illness Care Resources: Challenges of More Stringent Diagnostic Criteria for Prospective Enrollment.

Background: For patients with serious illnesses, one aim of palliative care services is to reduce the frequency and severity of hospital-based episodes of care. Since hospital-alternative palliative care may consume costly resources, providers need to efficiently target high-intensity services toward those most at risk for such adverse episodes of care. Objective: Our objective was to investigate progressively more restrictive diagnosis-based indications of serious illness as used to prospectively identify patients with higher average rates of hospitalization. Design/Setting: We designed an observational cohort quality improvement study using historical Medicare claims records to evaluate diagnostic inclusion criteria for targeting palliative and serious illness care resources. We first isolated a Seriously Ill Population (SIP) and then defined More SIP and Most SIP subgroups. Measurements: Our primary outcome measure was the 2019 acute-care count of hospitalizations for patients in the SIP, More SIP, and Most SIP subgroups, respectively. Results: The More SIP and Most SIP subgroups exhibited higher hospitalization rates. However, they also excluded progressively more seriously ill patients who did experience hospitalizations. In addition, almost half of the Most SIP subgroup were not hospitalized at all, despite having an average hospitalization rate greater than one. Conclusion:Allocating resources (personnel and services) toward reducing hospitalizations when almost half of the targeted population never goes to the hospital could result in unnecessary expenditures and exclude patients that could potentially benefit. Engaging community-based services to detect changes in status could provide supplemental indications of when and for whom to target palliative care resources.

Incident cardiovascular disease risk among older Asian, Native Hawaiian and Pacific Islander liver cancer survivors.

BACKGROUND: Cardiovascular disease (CVD) is a significant global health concern, particularly among Asian, Native Hawaiian, and Pacific Islander (ANHPI) communities that face unique health challenges. Liver cancer disproportionately affects ANHPI populations and has intricate associations with CVD risks due to shared pathophysiological mechanisms and metabolic disturbances. However, the specific CVD risk profile of ANHPI liver cancer patients remains poorly understood. METHODS: Using Surveillance, Epidemiology, and End Results (SEER)-Medicare data, we identified and matched 1150 ANHPI and 2070 Non-Hispanic White (NHW) liver cancer patients diagnosed between 2000 and 2017. We used the Fine-Gray sub-distribution hazard model to estimate hazard ratios (HRs) and 95 % confidence intervals (95 % CIs) for CVD risks, including ischemic heart disease (IHD), heart failure, and stroke, among ANHPI liver cancer patients compared to NHW counterparts and among ANHPI subgroups. RESULTS: ANHPI liver cancer patients demonstrated a lower risk of IHD compared to NHW counterparts (HR, 0.65, 95 % CI, 0.50, 0.86), aligning with broader trends. Subgroup analysis revealed notable heterogeneity within ANHPI populations, with Southeast Asian (HR, 0.65, 95 % CI, 0.42, 1.00) and Chinese patients (HR, 0.53, 95 % CI, 0.33-0.83) exhibiting lower IHD risks compared to their NHW counterparts. However, Native Hawaiian and Pacific Islander liver cancer patients showed elevated risks of heart failure (HR, 3.16, 95 % CI, 1.35-7.39) and IHD (HR, 5.64, 95 % CI, 2.19-14.53) compared to their Chinese counterparts. CONCLUSION: Our study highlights the complexity of CVD risks among ANHPI liver cancer patients. Addressing these disparities is crucial for improving cardiovascular outcomes and reducing the burden of CVD among ANHPI liver cancer patients.

Socioeconomic and Demographic Determinants of Readmission Rates in Congestive Heart Failure Patients: Insights From the Nationwide Readmissions Database.

Background Congestive heart failure (CHF) is a leading cause of hospitalizations and readmissions, placing a significant burden on the healthcare system. Identifying factors associated with readmission risk is crucial for developing targeted interventions and improving patient outcomes. This study aimed to investigate the impact of socioeconomic and demographic factors on 30-day and 90-day readmission rates in patients primarily admitted for CHF. Methods The study was carried out using a cross-sectional study design, and the data were obtained from the Nationwide Readmissions Database (NRD) from 2016 to 2020. Adult patients with a primary diagnosis of CHF were included. The primary outcomes were 30-day and 90-day all-cause readmission rates. Multivariable logistic regression was used to identify factors independently associated with readmissions, including race, ethnicity, insurance status, income level, and living arrangements. Results A total of 219,904 patients with a primary diagnosis of CHF were used in the study. The overall 30-day and 90-day readmission rates were 17.3% and 23.1%, respectively. In multivariable analysis, factors independently associated with higher 30-day readmission risk included Hispanic ethnicity (OR 1.18, 95% CI 1.03-1.35), African American race (OR 1.15, 95% CI 1.04-1.28), Medicare insurance (OR 1.24, 95% CI 1.12-1.38), and urban residence (OR 1.11, 95% CI 1.02-1.21). Higher income was associated with lower readmission risk (OR 0.87, 95% CI 0.79-0.96 for highest vs. lowest quartile). Similar patterns were observed for 90-day readmissions. Conclusion Socioeconomic and demographic factors, including race, ethnicity, insurance status, income level, and living arrangements, significantly impact 30-day and 90-day readmission rates in patients with CHF. These findings highlight the need for targeted interventions and policies that address social determinants of health and promote health equity in the management of CHF. Future research should focus on developing and evaluating culturally sensitive, community-based strategies to reduce readmissions and improve outcomes for high-risk CHF patients.

Evaluation of Access Disparities to Biologic Disease-Modifying Antirheumatic Drugs in Rural and Urban Communities.

The American College of Rheumatology guidelines provides a strong recommendation for the use of biologic disease-modifying antirheumatic drugs (bDMARDs) when conventional rheumatoid arthritis treatments fail to meet treatment targets. Although bDMARDs are an effective and important treatment component, access inequalities remain a challenge in many communities worldwide. The purpose of this analysis is to assess nationwide trends in bDMARD access in the United States, with a specific focus on rural and urban access gaps. This study combined multiple county-level databases to assess bDMARD prescriptions from 2015 to 2019. Using geospatial analysis and the Moran's I statistic, counties were classified according to prescription levels to assess for hotspots and coldspots. Analysis of variance (ANOVA) was used to compare significant counties across 49 socioeconomic variables of interest. The analysis identified statistically significant hotspot and coldspot prescription clusters within the United States. Coldspot (Low-Low) clusters with low access to bDMARDs are located predominantly in the rural west North Central region, extending down to Oklahoma and Arkansas. Hotspot (High-High) clusters are seen in urban and metro areas of Wisconsin, Minnesota, Pennsylvania, North Carolina, Georgia, Oregon, and the southern tip of Texas. Comparing coldspot to hotspot areas of bDMARD access revealed that the Medicare populations were older, more rural, less educated, less impoverished, and less likely to get their bDMARDs from a rheumatologist.

Hospital Outcomes in Uninsured Patients With Disease and Disorders of Nervous System: A National Cohort Study During a Decade in the United States.

Objectives Health insurance is associated with better outcomes in the admitted patient population, even after adjusting for other factors such as race and socioeconomic status. However, the literature is limited on the relationship between insurance status and hospital outcomes in patients hospitalized with the disease of nervous system. Methods This cross-sectional study used the Nationwide Inpatient Sample (NIS) database to achieve the results. All Major Diagnostic Category (MDC) codes from patients discharged for disease and disorders of nervous system between the years 2005 to 2014 were queried and analyzed for the impact of lack of insurance on patient outcome. Results Among 4,737,999 discharges, 5.6% had no insurance. The hospital mortality rate among uninsured and insured patients was 4.1% and 3.7%, respectively (P<0.001). In the multivariate analysis, hospital mortality of uninsured patients was higher in the elderly (aOR: 4.74[CI:4.52-4.97], P<0.001), those with comorbidities (aOR: 2.23[CI:2.18-2.27], P<0.001), Asians (aOR: 1.16[CI:1.12-1.20]. P<0.001), in rural areas (aOR: 1.44[ 95%CI:1.41-1.48], P<0.001) and those in the lowest household income quartile (aOR: 1.03[CI:1.01-1.05], P<0.001). The average length of stay (LOS) was shorter for the uninsured (4.79±8.26 vs 4.96±7.55 days, P<0.001). Conclusions The findings suggest that lack of health insurance is correlated with hospital mortality in patients hospitalized with disease and disorders of nervous system, with an increased disparity in vulnerable populations.

Comparison of physician networks constructed from thresholded ties versus shared clinical episodes.

OBJECTIVE: To compare standard methods for constructing physician networks from patient-physician encounter data with a new method based on clinical episodes of care. DATA SOURCE: We used data on 100% of traditional Medicare beneficiaries from 51 nationally representative geographical regions for the years 2005-2010. STUDY DESIGN: We constructed networks of physicians based on their shared patients. In the fixed-threshold networks and adaptive-threshold networks, we included data on all patient-physician encounters to form the physician-physician ties, and then subsequently thresholded some proportion of the strongest ties. In contrast, in the episode-based approach, only those patient-physician encounters that occurred within shared clinical episodes treating specific conditions contributed towards physician-physician ties. DATA COLLECTION/EXTRACTION METHODS: We extracted clinical episodes in the Medicare data and investigated structural properties of the patient-sharing networks of physicians, temporal dynamics of their ties, and temporal stability of network communities across the two approaches. PRINCIPAL FINDINGS: The episode-based networks accentuated ties between primary care physicians (PCPs) and medical specialists, had ties that were more likely to reappear in the future, and appeared to have more fluid community structure. CONCLUSIONS: Constructing physician networks around shared episodes of care is a clinically sound alternative to previous approaches to network construction that does not require arbitrary decisions about thresholding. The resulting networks capture somewhat different aspects of patient-physician encounters.

Big Data, Big Research: Implementing Population Health-Based Research Models and Integrating Care to Reduce Cost and Improve Outcomes.

Recent trends in clinical research have moved attention toward reporting clinical outcomes and resource consumption associated with various care processes. This change is the result of technological advancement and a national effort to critically assess health care delivery. As orthopedic surgeons traverse an unchartered health care environment, a more complete understanding of how clinical research is conducted using large data sets is necessary. The purpose of this article is to review various advantages and disadvantages of large data sets available for orthopaedic use, examine their ideal use, and report how they are being implemented nationwide.

Circulatory Diseases in the U.S. Elderly in the Linked National Long-Term Care Survey-Medicare Database: Population-Based Analysis of Incidence, Comorbidity, and Disability.

Incidence rates of acute coronary heart disease (ACHD; including myocardial infarction and angina pectoris), stroke, and heart failure (HF) were studied for their age, disability, and comorbidity patterns in the U.S. elderly population using the National Long Term Care Survey (NLTCS) data linked to Medicare records for 1991-2005. Incidence rates increased with age with a decrease in the oldest old (stroke and HF) or were stable at all ages (ACHD). For all diseases, incidence rates were lower among institutionalized individuals and higher in individuals with higher comorbidity indices. The results could be used for understanding currently debated effects of biomedical research, screening, and therapeutic innovations on changes in disease incidence with advancing age as well as for projecting future Medicare costs.

Near/far matching: a study design approach to instrumental variables.

Classic instrumental variable techniques involve the use of structural equation modeling or other forms of parameterized modeling. In this paper we use a nonparametric, matching-based instrumental variable methodology that is based on a study design approach. Similar to propensity score matching, though unlike classic instrumental variable approaches, near/far matching is capable of estimating causal effects when the outcome is not continuous. Unlike propensity score matching, though similar to instrumental variable techniques, near/far matching is also capable of estimating causal effects even when unmeasured covariates produce selection bias. We illustrate near/far matching by using Medicare data to compare the effectiveness of carotid arterial stents with cerebral protection versus carotid endarterectomy for the treatment of carotid stenosis.