Impact of Hospital Affiliation With a Flagship Hospital System on Surgical Outcomes.

OBJECTIVE: To compare general surgery outcomes at flagship systems, flagship hospitals, and flagship hospital affiliates versus matched controls. SUMMARY BACKGROUND DATA: It is unknown whether flagship hospitals perform better than flagship hospital affiliates for surgical patients. METHODS: Using Medicare claims for 2018 to 2019, we matched patients undergoing inpatient general surgery in flagship system hospitals to controls who underwent the same procedure at hospitals outside the system but within the same region. We defined a "flagship hospital" within each region as the major teaching hospital with the highest patient volume that is also part of a hospital system; its system was labeled a "flagship system." We performed 4 main comparisons: patients treated at any flagship system hospital versus hospitals outside the flagship system; flagship hospitals versus hospitals outside the flagship system; flagship hospital affiliates versus hospitals outside the flagship system; and flagship hospitals versus affiliate hospitals. Our primary outcome was 30-day mortality. RESULTS: We formed 32,228 closely matched pairs across 35 regions. Patients at flagship system hospitals (32,228 pairs) had lower 30-day mortality than matched control patients [3.79% vs. 4.36%, difference=-0.57% (-0.86%, -0.28%), P<0.001]. Similarly, patients at flagship hospitals (15,571/32,228 pairs) had lower mortality than control patients. However, patients at flagship hospital affiliates (16,657/32,228 pairs) had similar mortality to matched controls. Flagship hospitals had lower mortality than affiliate hospitals [difference-in-differences=-1.05% (-1.62%, -0.47%), P<0.001]. CONCLUSIONS: Patients treated at flagship hospitals had significantly lower mortality rates than those treated at flagship hospital affiliates. Hence, flagship system affiliation does not alone imply better surgical outcomes.

Exposure to Operative Anesthesia in Childhood and Subsequent Neurobehavioral Diagnoses: A Natural Experiment using Appendectomy.

BACKGROUND: Observational studies of anesthetic neurotoxicity may be biased because children requiring anesthesia commonly have medical conditions associated with neurobehavioral problems. This study takes advantage of a natural experiment associated with appendicitis, in order to determine if anesthesia and surgery in childhood were specifically associated with subsequent neurobehavioral outcomes. METHODS: We identified 134,388 healthy children with appendectomy and examined the incidence of subsequent externalizing or behavioral disorders (conduct, impulse control, oppositional defiant, or attention-deficit/hyperactivity disorder); or internalizing or mood/anxiety disorders (depression, anxiety, or bipolar disorder) when compared to 671,940 matched healthy controls as identified in Medicaid data between 2001-2018. For comparison, we also examined 154,887 otherwise healthy children admitted to the hospital for pneumonia, cellulitis, and gastroenteritis, of which only 8% received anesthesia, and compared them to 774,435 matched healthy controls. We also examined the difference-in-differences between matched appendectomy patients and their controls and matched medical admission patients and their controls. RESULTS: Compared to controls, children with appendectomy were more likely to have subsequent behavioral disorders (the hazard ratio (HR) was 1.04 (95% CI 1.01, 1.06), P = 0.0010), and mood/anxiety disorders (HR: 1.15 (95% CI 1.13, 1.17), P < 0.0001). Relative to controls, children with medical admissions were also more likely to have subsequent behavioral (HR: 1.20 (95% CI 1.18, 1.22), P < 0.0001), and mood/anxiety (HR: 1.25 (95% CI 1.23, 1.27), P < 0.0001) disorders. Comparing the difference between matched appendectomy patients and their matched controls to the difference between matched medical patients and their matched controls, medical patients had more subsequent neurobehavioral problems than appendectomy patients. CONCLUSIONS: Although there is an association between neurobehavioral diagnoses and appendectomy, this association is not specific to anesthesia exposure, and is stronger in medical admissions. Medical admissions, generally without anesthesia exposure, displayed significantly higher rates of these disorders than appendectomy-exposed patients.

Optimal refinement of strata to balance covariates.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

The Safety of Performing Surgery at Ambulatory Surgery Centers Versus Hospital Outpatient Departments in Older Patients With or Without Multimorbidity.

BACKGROUND: Surgery for older Americans is increasingly being performed at ambulatory surgery centers (ASCs) rather than hospital outpatient departments (HOPDs), while rates of multimorbidity have increased. OBJECTIVE: To determine whether there are differential outcomes in older patients undergoing surgical procedures at ASCs versus HOPDs. RESEARCH DESIGN: Matched cohort study. SUBJECTS: Of Medicare patients, 30,958 were treated in 2018 and 2019 at an ASC undergoing herniorrhaphy, cholecystectomy, or open breast procedures, matched to similar HOPD patients, and another 32,702 matched pairs undergoing higher-risk procedures. MEASURES: Seven and 30-day revisit and complication rates. RESULTS: For the same procedures, HOPD patients displayed a higher baseline predicted risk of 30-day revisits than ASC patients (13.09% vs 8.47%, P < 0.0001), suggesting the presence of considerable selection on the part of surgeons. In matched Medicare patients with or without multimorbidity, we observed worse outcomes in HOPD patients: 30-day revisit rates were 8.1% in HOPD patients versus 6.2% in ASC patients ( P < 0.0001), and complication rates were 41.3% versus 28.8%, P < 0.0001. Similar patterns were also found for 7-day outcomes and in higher-risk procedures examined in a secondary analysis. Similar patterns were also observed when analyzing patients with and without multimorbidity separately. CONCLUSIONS: The rates of revisits and complications for ASC patients were far lower than for closely matched HOPD patients. The observed initial baseline risk in HOPD patients was much higher than the baseline risk for the same procedures performed at the ASC, suggesting that surgeons are appropriately selecting their riskier patients to be treated at the HOPD rather than the ASC.

Defining Multimorbidity in Older Patients Hospitalized with Medical Conditions.

BACKGROUND: The term "multimorbidity" identifies high-risk, complex patients and is conventionally defined as ≥2 comorbidities. However, this labels almost all older patients as multimorbid, making this definition less useful for physicians, hospitals, and policymakers. OBJECTIVE: Develop new medical condition-specific multimorbidity definitions for patients admitted with acute myocardial infarction (AMI), heart failure (HF), and pneumonia patients. We developed three medical condition-specific multimorbidity definitions as the presence of single, double, or triple combinations of comorbidities - called Qualifying Comorbidity Sets (QCSs) - associated with at least doubling the risk of 30-day mortality for AMI and pneumonia, or one-and-a-half times for HF patients, compared to typical patients with these conditions. DESIGN: Cohort-based matching study PARTICIPANTS: One hundred percent Medicare Fee-for-Service beneficiaries with inpatient admissions between 2016 and 2019 for AMI, HF, and pneumonia. MAIN MEASURES: Thirty-day all-location mortality KEY RESULTS: We defined multimorbidity as the presence of ≥1 QCS. The new definitions labeled fewer patients as multimorbid with a much higher risk of death compared to the conventional definition (≥2 comorbidities). The proportions of patients labeled as multimorbid using the new definition versus the conventional definition were: for AMI 47% versus 87% (p value<0.0001), HF 53% versus 98% (p value<0.0001), and pneumonia 57% versus 91% (p value<0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p<0.0001), HF 9.9% versus 7.0% (p <0.0001), and pneumonia 18.4% versus 13.2% (p <0.0001). CONCLUSION: The presence of ≥2 comorbidities identified almost all patients as multimorbid. In contrast, our new QCS-based definitions selected more specific combinations of comorbidities associated with substantial excess risk in older patients admitted for AMI, HF, and pneumonia. Thus, our new definitions offer a better approach to identifying multimorbid patients, allowing physicians, hospitals, and policymakers to more effectively use such information to consider focused interventions for these vulnerable patients.

Mortality Among Older Medical Patients at Flagship Hospitals and Their Affiliates.

BACKGROUND: We define a "flagship hospital" as the largest academic hospital within a hospital referral region and a "flagship system" as a system that contains a flagship hospital and its affiliates. It is not known if patients admitted to an affiliate hospital, and not to its main flagship hospital, have better outcomes than those admitted to a hospital outside the flagship system but within the same hospital referral region. OBJECTIVE: To compare mortality at flagship hospitals and their affiliates to matched control patients not in the flagship system but within the same hospital referral region. DESIGN: A matched cohort study PARTICIPANTS: The study used hospitalizations for common medical conditions between 2018-2019 among older patients age ≥ 66 years. We analyzed 118,321 matched pairs of Medicare patients admitted with pneumonia (N=57,775), heart failure (N=42,531), or acute myocardial infarction (N=18,015) in 35 flagship hospitals, 124 affiliates, and 793 control hospitals. MAIN MEASURES: 30-day (primary) and 90-day (secondary) all-cause mortality. KEY RESULTS: 30-day mortality was lower among patients in flagship systems versus control hospitals that are not part of the flagship system but within the same hospital referral region (difference= -0.62%, 95% CI [-0.88%, -0.37%], P<0.001). This difference was smaller in affiliates versus controls (-0.43%, [-0.75%, -0.11%], P=0.008) than in flagship hospitals versus controls (-1.02%, [-1.46%, -0.58%], P<0.001; difference-in-difference -0.59%, [-1.13%, -0.05%], P=0.033). Similar results were found for 90-day mortality. LIMITATIONS: The study used claims-based data. CONCLUSIONS: In aggregate, within a hospital referral region, patients treated at the flagship hospital, at affiliates of the flagship hospital, and in the flagship system as a whole, all had lower mortality rates than matched controls outside the flagship system. However, the mortality advantage was larger for flagship hospitals than for their affiliates.

Sensitivity analyses informed by tests for bias in observational studies.

In an observational study, the treatment received and the outcome exhibited may be associated in the absence of an effect caused by the treatment, even after controlling for observed covariates. Two tactics are common: (i) a test for unmeasured bias may be obtained using a secondary outcome for which the effect is known and (ii) a sensitivity analysis may explore the magnitude of unmeasured bias that would need to be present to explain the observed association as something other than an effect caused by the treatment. Can such a test for unmeasured bias inform the sensitivity analysis? If the test for bias does not discover evidence of unmeasured bias, then ask: Are conclusions therefore insensitive to larger unmeasured biases? Conversely, if the test for bias does find evidence of bias, then ask: What does that imply about sensitivity to biases? This problem is formulated in a new way as a convex quadratically constrained quadratic program and solved on a large scale using interior point methods by a modern solver. That is, a convex quadratic function of N variables is minimized subject to constraints on linear and convex quadratic functions of these variables. The quadratic function that is minimized is a statistic for the primary outcome that is a function of the unknown treatment assignment probabilities. The quadratic function that constrains this minimization is a statistic for subsidiary outcome that is also a function of these same unknown treatment assignment probabilities. In effect, the first statistic is minimized over a confidence set for the unknown treatment assignment probabilities supplied by the unaffected outcome. This process avoids the mistake of interpreting the failure to reject a hypothesis as support for the truth of that hypothesis. The method is illustrated by a study of the effects of light daily alcohol consumption on high-density lipoprotein (HDL) cholesterol levels. In this study, the method quickly optimizes a nonlinear function of N = 800 $N=800$ variables subject to linear and quadratic constraints. In the example, strong evidence of unmeasured bias is found using the subsidiary outcome, but, perhaps surprisingly, this finding makes the primary comparison insensitive to larger biases.

A second evidence factor for a second control group.

In an observational study of the effects caused by a treatment, a second control group is used in an effort to detect bias from unmeasured covariates, and the investigator is content if no evidence of bias is found. This strategy is not entirely satisfactory: two control groups may differ significantly, yet the difference may be too small to invalidate inferences about the treatment, or the control groups may not differ yet nonetheless fail to provide a tangible strengthening of the evidence of a treatment effect. Is a firmer conclusion possible? Is there a way to analyze a second control group such that the data might report measurably strengthened evidence of cause and effect, that is, insensitivity to larger unmeasured biases? Evidence factor analyses are not commonly used with a second control group: most analyses compare the treated group to each control group, but analyses of that kind are partially redundant; so, they do not constitute evidence factors. An alternative analysis is proposed here, one that does yield two evidence factors, and with a carefully designed test statistic, is capable of extracting strong evidence from the second factor. The new technical work here concerns the development of a test statistic with high design sensitivity and high Bahadur efficiency in a sensitivity analysis for the second factor. A study of binge drinking as a cause of high blood pressure is used as an illustration.

Alzheimer's Dementia After Exposure to Anesthesia and Surgery in the Elderly: A Matched Natural Experiment Using Appendicitis.

OBJECTIVE: The aim of this study was to determine whether surgery and anesthesia in the elderly may promote Alzheimer disease and related dementias (ADRD). BACKGROUND: There is a substantial conflicting literature concerning the hypothesis that surgery and anesthesia promotes ADRD. Much of the literature is confounded by indications for surgery or has small sample size. This study examines elderly patients with appendicitis, a common condition that strikes mostly at random after controlling for some known associations. METHODS: A matched natural experiment of patients undergoing appendectomy for appendicitis versus control patients without appendicitis using Medicare data from 2002 to 2017, examining 54,996 patients without previous diagnoses of ADRD, cognitive impairment, or neurological degeneration, who developed appendicitis between ages 68 through 77 years and underwent an appendectomy (the ''Appendectomy'' treated group), matching them 5:1 to 274,980 controls, examining the subsequent hazard for developing ADRD. RESULTS: The hazard ratio (HR) for developing ADRD or death was lower in the Appendectomy group than controls: HR = 0.96 [95% confidence interval (CI) 0.94-0.98], P < 0.0001, (28.2% in Appendectomy vs 29.1% in controls, at 7.5 years). The HR for death was 0.97 (95% CI 0.95-0.99), P = 0.002, (22.7% vs 23.1% at 7.5 years). The HR for developing ADRD alone was 0.89 (95% CI 0.86-0.92), P < 0.0001, (7.6% in Appendectomy vs 8.6% in controls, at 7.5 years). No subgroup analyses found significantly elevated rates of ADRD in the Appendectomy group. CONCLUSION: In this natural experiment involving 329,976 elderly patients, exposure to appendectomy surgery and anesthesia did not increase the subsequent rate of ADRD.

A statistic with demonstrated insensitivity to unmeasured bias for 2 × 2 × S tables in observational studies.

Are weak associations between a treatment and a binary outcome always sensitive to small unmeasured biases in observational studies? This possibility is often discussed in epidemiology. The familiar Mantel-Haenszel test for a 2 × 2 × S $$ 2\times 2\times S $$ contingency table exaggerates sensitivity to unmeasured biases when the population odds ratios vary among the S $$ S $$ strata. A statistic built from several components, here from the S $$ S $$  strata, is said to have demonstrated insensitivity to bias if it uses only those components that provide indications of insensitivity to bias. Briefly, such a statistic is a d $$ d $$ -statistic. There are 2 S - 1 $$ {2}^S-1 $$ candidate statistics with S $$ S $$ strata, and a d $$ d $$ -statistic considers them all.  To have level α $$ \alpha $$ , a test based on a d $$ d $$ -statistic must pay a price for its double use of the data, but as the sample size increases, that price becomes small, while the gain may be large. The price is paid by conditioning on the limited information used to identify components that are insensitive to a bias of specified magnitude, basing the test result on the information that remains after conditioning. In large samples, the d $$ d $$ -statistic achieves the largest possible design sensitivity, so it does not exaggerate sensitivity to unmeasured bias. A simulation verifies that the large sample result has traction in samples of practical size. A study of sunlight as a cause of cataract is used to illustrate issues and methods. Several extensions of the method are discussed. An R package dstat2x2xk implements the method.

A National Comparison of Operative Outcomes of New and Experienced Surgeons.

OBJECTIVE: To determine whether outcomes achieved by new surgeons are attributable to inexperience or to differences in the context in which care is delivered and patient complexity. BACKGROUND: Although prior studies suggest that new surgeon outcomes are worse than those of experienced surgeons, factors that underlie these phenomena are poorly understood. METHODS: A nationwide observational tapered matching study of outcomes of Medicare patients treated by new and experienced surgeons in 1221 US hospitals (2009-2013). The primary outcome studied is 30-day mortality. Secondary outcomes were examined. RESULTS: In total, 694,165 patients treated by 8503 experienced surgeons were matched to 68,036 patients treated by 2119 new surgeons working in the same hospitals. New surgeons' patients were older (25.8% aged ≥85 vs 16.3%,P<0.0001) with more emergency admissions (53.9% vs 25.8%,P<0.0001) than experienced surgeons' patients. Patients of new surgeons had a significantly higher baseline 30-day mortality rate compared with patients of experienced surgeons (6.2% vs 4.5%,P<0.0001;OR 1.42 (1.33, 1.52)). The difference remained significant after matching the types of operations performed (6.2% vs 5.1%, P<0.0001; OR 1.24 (1.16, 1.32)) and after further matching on a combination of operation type and emergency admission status (6.2% vs 5.6%, P=0.0007; OR 1.12 (1.05, 1.19)). After matching on operation type, emergency admission status, and patient complexity, the difference between new and experienced surgeons' patients' 30-day mortality became indistinguishable (6.2% vs 5.9%,P=0.2391;OR 1.06 (0.97, 1.16)). CONCLUSIONS: Among Medicare beneficiaries, the majority of the differences in outcomes between new and experienced surgeons are related to the context in which care is delivered and patient complexity rather than new surgeon inexperience.

Combining planned and discovered comparisons in observational studies.

In observational studies of treatment effects, it is common to have several outcomes, perhaps of uncertain quality and relevance, each purporting to measure the effect of the treatment. A single planned combination of several outcomes may increase both power and insensitivity to unmeasured bias when the plan is wisely chosen, but it may miss opportunities in other cases. A method is proposed that uses one planned combination with only a mild correction for multiple testing and exhaustive consideration of all possible combinations fully correcting for multiple testing. The method works with the joint distribution of $\kappa^{T}\left( \mathbf{T}-\boldsymbol{\mu}\right) /\sqrt {\boldsymbol{\kappa}^{T}\boldsymbol{\Sigma\boldsymbol{\kappa}}}$ and $max_{\boldsymbol{\lambda}\neq\mathbf{0}}$$\,\lambda^{T}\left( \mathbf{T} -\boldsymbol{\mu}\right) /$$\sqrt{\boldsymbol{\lambda}^{T}\boldsymbol{\Sigma \lambda}}$ where $\kappa$ is chosen a priori and the test statistic $\mathbf{T}$ is asymptotically $N_{L}\left( \boldsymbol{\mu},\boldsymbol{\Sigma}\right) $. The correction for multiple testing has a smaller effect on the power of $\kappa^{T}\left( \mathbf{T}-\boldsymbol{\mu }\right) /\sqrt{\boldsymbol{\kappa}^{T}\boldsymbol{\Sigma\boldsymbol{\kappa} }}$ than does switching to a two-tailed test, even though the opposite tail does receive consideration when $\lambda=-\kappa$. In the application, there are three measures of cognitive decline, and the a priori comparison $\kappa$ is their first principal component, computed without reference to treatment assignments. The method is implemented in an R package sensitivitymult.

Evaluating the Costs and Outcomes of Hospital Nursing Resources: a Matched Cohort Study of Patients with Common Medical Conditions.

BACKGROUND: Nursing resources, such as staffing ratios and skill mix, vary across hospitals. Better nursing resources have been linked to better patient outcomes but are assumed to increase costs. The value of investments in nursing resources, in terms of clinical benefits relative to costs, is unclear. OBJECTIVE: To determine whether there are differential clinical outcomes, costs, and value among medical patients at hospitals characterized by better or worse nursing resources. DESIGN: Matched cohort study of patients in 306 acute care hospitals. PATIENTS: A total of 74,045 matched pairs of fee-for-service Medicare beneficiaries admitted for common medical conditions (25,446 sepsis pairs; 16,332 congestive heart failure pairs; 12,811 pneumonia pairs; 10,598 stroke pairs; 8858 acute myocardial infarction pairs). Patients were also matched on hospital size, technology, and teaching status. MAIN MEASURES: Better (n = 76) and worse (n = 230) nursing resourced hospitals were defined by patient-to-nurse ratios, skill mix, proportions of bachelors-degree nurses, and nurse work environments. Outcomes included 30-day mortality, readmission, and resource utilization-based costs. KEY RESULTS: Patients in hospitals with better nursing resources had significantly lower 30-day mortality (16.1% vs 17.1%, p < 0.0001) and fewer readmissions (32.3% vs 33.6%, p < 0.0001) yet costs were not significantly different ($18,848 vs 18,671, p = 0.133). The greatest outcomes and cost advantage of better nursing resourced hospitals were in patients with sepsis who had lower mortality (25.3% vs 27.6%, p < 0.0001). Overall, patients with the highest risk of mortality on admission experienced the greatest reductions in mortality and readmission from better nursing at no difference in cost. CONCLUSIONS: Medicare beneficiaries with common medical conditions admitted to hospitals with better nursing resources experienced more favorable outcomes at almost no difference in cost.

Using Evidence Factors to Clarify Exposure Biomarkers.

A study has 2 evidence factors if it permits 2 statistically independent inferences about 1 treatment effect such that each factor is immune to some bias that would invalidate the other factor. Because the 2 factors are statistically independent, the evidence they provide can be combined using methods associated with meta-analysis for independent studies, despite using the same data twice in different ways. We illustrate evidence factors, applying them in a new way in investigations that have both an exposure biomarker and a coarse external measure of exposure to a treatment. To illustrate, we consider the possible effects of cigarette smoking on homocysteine levels, with self-reported smoking and a cotinine biomarker. We examine joint sensitivity of 2 factors to bias from confounding, a central aspect of any observational study.

Comparing Outcomes and Costs of Surgical Patients Treated at Major Teaching and Nonteaching Hospitals: A National Matched Analysis.

OBJECTIVE: To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics. BACKGROUND: Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear. METHODS: A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios < 0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery. RESULTS: In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%, <0.0001), and overall paired cost difference = $915 (P < 0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P < 0.0001), and paired cost difference = $3773 (P < 0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals. CONCLUSIONS: Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.

Duty Hour Reform and the Outcomes of Patients Treated by New Surgeons.

OBJECTIVE: The aim of the study was to address the controversy surrounding the effects of duty hour reform on new surgeon performance, we analyzed patients treated by new surgeons following the transition to independent practice. SUMMARY BACKGROUND DATA: In 2003, duty hour reform affected all US surgical training programs. Its impact on the performance of new surgeons remains unstudied. METHODS: We studied 30-day mortality among 1,483,074 Medicare beneficiaries undergoing general and orthopedic operations between 1999 and 2003 ("traditional" era) and 2009 and 2013 ("modern" era). The operations were performed by 2762 new surgeons trained before the reform, 2119 new surgeons trained following reform and 15,041 experienced surgeons. We used a difference-in-differences analysis comparing outcomes in matched patients treated by new versus experienced surgeons within each era, controlling for the hospital, operation, and patient risk factors. RESULTS: Traditional era odds of 30-day mortality among matched patients treated by new versus experienced surgeons were significantly elevated [odds ratio (OR) 1.13; 95% confidence interval (CI) (1.05, 1.22), P < 0.001). The modern era elevated odds of mortality were not significant [OR 1.06; 95% CI (0.97-1.16), P = 0.239]. Relative performance of new and experienced surgeons with respect to 30-day mortality did not appear to change from the traditional era to the modern era [OR 0.93; 95% CI (0.83-1.05), P = 0.233]. There were statistically significant adverse changes over time in relative performance to experienced surgeons in prolonged length of stay [OR 1.08; 95% CI (1.02-1.15), P = 0.015], anesthesia time [9 min; 95% CI (8-10), P < 0.001], and costs [255USD; 95% CI (2-508), P = 0.049]. CONCLUSIONS: Duty hour reform showed no significant effect on 30-day mortality achieved by new surgeons compared to their more experienced colleagues. Patients of new surgeons, however, trained after duty hour reform displayed some increases in the resources needed for their care.

Comparing Outcomes and Costs of Medical Patients Treated at Major Teaching and Non-teaching Hospitals: A National Matched Analysis.

BACKGROUND: Teaching hospitals typically pioneer investment in new technology and cultivate workforce characteristics generally associated with better quality, but the value of this extra investment is unclear. OBJECTIVE: Compare outcomes and costs between major teaching and non-teaching hospitals by closely matching on patient characteristics. DESIGN: Medicare patients at 339 major teaching hospitals (resident-to-bed (RTB) ratios ≥ 0.25); matched patient controls from 2439 non-teaching hospitals (RTB ratios < 0.05). PARTICIPANTS: Forty-three thousand nine hundred ninety pairs of patients (one from a major teaching hospital and one from a non-teaching hospital) admitted for acute myocardial infarction (AMI), 84,985 pairs admitted for heart failure (HF), and 74,947 pairs admitted for pneumonia (PNA). EXPOSURE: Treatment at major teaching hospitals versus non-teaching hospitals. MAIN MEASURES: Thirty-day all-cause mortality, readmissions, ICU utilization, costs, payments, and value expressed as extra cost for a 1% improvement in survival. KEY RESULTS: Thirty-day mortality was lower in teaching than non-teaching hospitals (10.7% versus 12.0%, difference = - 1.3%, P < 0.0001). The paired cost difference (teaching - non-teaching) was $273 (P < 0.0001), yielding $211 per 1% mortality improvement. For the quintile of pairs with highest risk on admission, mortality differences were larger (24.6% versus 27.6%, difference = - 3.0%, P < 0.0001), and paired cost difference = $1289 (P < 0.0001), yielding $427 per 1% mortality improvement at 30 days. Readmissions and ICU utilization were lower in teaching hospitals (both P < 0.0001), but length of stay was longer (5.5 versus 5.1 days, P < 0.0001). Finally, individual results for AMI, HF, and PNA showed similar findings as in the combined results. CONCLUSIONS AND RELEVANCE: Among Medicare patients admitted for common medical conditions, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used.

Using Medicare claims in identifying Alzheimer's disease and related dementias.

INTRODUCTION: This study develops a measure of Alzheimer's disease and related dementias (ADRD) using Medicare claims. METHODS: Validation resembles the approach of the American Psychological Association, including (1) content validity, (2) construct validity, and (3) predictive validity. RESULTS: We found that four items-a Medicare claim recording ADRD 1 year ago, 2 years ago, 3 years ago, and a total stay of 6 months in a nursing home-exhibit a pattern of association consistent with a single underlying ADRD construct, and presence of any two of these four items predict a direct measure of cognitive function and also future claims for ADRD. DISCUSSION: Our four items are internally consistent with the measurement of a single quantity. The presence of any two items do a better job than a single claim when predicting both a direct measure of cognitive function and future ADRD claims.

Comparing Resource Use in Medical Admissions of Children With Complex Chronic Conditions.

BACKGROUND: Children with complex chronic conditions (CCCs) utilize a disproportionate share of hospital resources. OBJECTIVE: We asked whether some hospitals display a significantly different pattern of resource utilization than others when caring for similar children with CCCs admitted for medical diagnoses. RESEARCH DESIGN: Using Pediatric Health Information System data from 2009 to 2013, we constructed an inpatient Template of 300 children with CCCs, matching these to 300 patients at each hospital, thereby performing a type of direct standardization. SUBJECTS: Children with CCCs were drawn from a list of the 40 most common medical principal diagnoses, then matched to patients across 40 Children's Hospitals. MEASURES: Rate of intensive care unit admission, length of stay, resource cost. RESULTS: For the Template-matched patients, when comparing resource use at the lower 12.5-percentile and upper 87.5-percentile of hospitals, we found: intensive care unit utilization was 111% higher (6.6% vs. 13.9%, P<0.001); hospital length of stay was 25% higher (2.4 vs. 3.0 d/admission, P<0.001); and finally, total cost per patient varied by 47% ($6856 vs. $10,047, P<0.001). Furthermore, some hospitals, compared with their peers, were more efficient with low-risk patients and less efficient with high-risk patients, whereas other hospitals displayed the opposite pattern. CONCLUSIONS: Hospitals treating similar patients with CCCs admitted for similar medical diagnoses, varied greatly in resource utilization. Template Matching can aid chief quality officers benchmarking their hospitals to peer institutions and can help determine types of their patients having the most aberrant outcomes, facilitating quality initiatives to target these patients.

Directional penalties for optimal matching in observational studies.

Multivariate matching in observational studies tends to view covariate differences symmetrically: a difference in age of 10 years is thought equally problematic whether the treated subject is older or younger than the matched control. If matching is correcting an imbalance in age, such that treated subjects are typically older than controls, then the situation in need of correction is asymmetric: a matched pair with a difference in age of 10 years is much more likely to have an older treated subject and a younger control than the opposite. Correcting the bias may be easier if matching tries to avoid the typical case that creates the bias. We describe several easily used, asymmetric, directional penalties and illustrate how they can improve covariate balance in a matched sample. The investigator starts with a matched sample built in a conventional way, then diagnoses residual covariate imbalances in need of reduction, and achieves the needed reduction by slightly altering the distance matrix with directional penalties, creating a new matched sample. Unlike penalties commonly used in matching, a directional penalty can go too far, reversing the direction of the bias rather than reducing the bias, so the magnitude of the directional penalty matters and may need adjustment. Our experience is that two or three adjustments, guided by balance diagnostics, can substantially improve covariate balance, perhaps requiring fifteen minutes effort sitting at the computer. We also explore the connection between directional penalties and a widely used technique in integer programming, namely Lagrangian relaxation of problematic linear side constraints in a minimum cost flow problem. In effect, many directional penalties are Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. The method and example are in an R package DiPs at CRAN.