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OBJECTIVE: To determine if surgery and anesthesia in the elderly may promote Alzheimer’s 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% CI 0.94, 0.98), P < 0.0001, (28.2% in Appendectomy versus 29.1% in controls, at 7.5 years). The HR for death was 0.97 (0.95, 0.99), P = 0.002, (22.7% versus 23.1% at 7.5 years). The HR for developing ADRD alone was 0.89 (0.86, 0.92), P < 0.0001, (7.6% in Appendectomy versus 8.6% in controls, at 7.5 years). No subgroup analyses found significantly elevated rates of ADRD in the Appendectomy group. CONCLUSIONS: In this natural experiment involving 329,976 elderly patients, exposure to appendectomy surgery and anesthesia did not increase the subsequent rate of ADRD.

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.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.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.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 value0.0001), HF 53% versus 98% (p value0.0001), and pneumonia 57% versus 91% (p value0.0001). Thirty-day mortality was higher among patients with ≥1 QCS compared to ≥2 comorbidities: for AMI 15.0% versus 9.5% (p0.0001), HF 9.9% versus 7.0% (p0.0001), and pneumonia 18.4% versus 13.2% (p0.0001).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.

A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce? Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

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

In causal inference, natural strata are a new compromise between conventional strata and matching in a fixed ratio, say pair matching or matching two controls to each treated individual. Like matching in a fixed ratio, natural strata: (a) do not require weights, (b) balance many measured covariates beyond those that define the strata and (c) provide closer balance for a measured continuous covariate coarsely cut to form strata. Unlike matching in a fixed ratio, the ratio of controls to treated individuals need not be an integer, so if the data permit a fixed ratio comparison of 1-to-2.5 or even 1-to-0.75, then these ratios are possible using natural strata. Optimal natural strata are defined by a moderate number of fixed strata plus an integer program that minimizes the imbalance in many other measured covariates that are not used to specify the strata. Solving large integer programs is computationally difficult. A tool in the theory of approximation algorithms is ‘randomized rounding of a linear program’ to produce an integer solution: a fractional solution to a linear program defines a probability distribution for an integer-valued random variable which is sampled. We apply this tool in a new way to produce natural strata and develop new properties of randomized rounding in this context. When proportional strata are impractical, we approximate them by minimizing the earthmover distance to proportionality. The method is applied to study birth outcomes for older and younger mothers in the United States in 2018. An R package natstrat is available at CRAN.

Multivariate matching has two goals (i) to construct treated and control groups that have similar distributions of observed covariates, and (ii) to produce matched pairs or sets that are homogeneou...

ObjectivesEvaluate whether hospital factors, including nurse resources, explain racial differences in Medicare black and white patient surgical outcomes and whether disparities changed over time.DesignRetrospective tapered-match.Setting571 hospitals at two time points (Early Era 2003–2005; Recent Era 2013–2015).Participants6752 black patients and three sets of 6752 white controls selected from 107 001 potential controls (Early Era). 4964 black patients and three sets of 4964 white controls selected from 74 108 potential controls (Recent Era).InterventionsBlack patients were matched to white controls on demographics (age, sex, state and year of procedure), procedure (demographics variables plus 136 International Classification of Diseases (ICD)-9 principal procedure codes) and presentation (demographics and procedure variables plus 34 comorbidities, a mortality risk score, a propensity score for being black, emergency admission, transfer status, predicted procedure time).Outcomes30-day and 1-year mortality.ResultsBefore matching, black patients had more comorbidities, higher risk of mortality despite being younger and underwent procedures at different percentages than white patients. Whites in the demographics match had lower mortality at 30 days (5.6% vs 6.7% Early Era; 5.4% vs 5.7% Recent Era) and 1-year (15.5% vs 21.5% Early Era; 12.3% vs 15.9% Recent Era). Black–white 1-year mortality differences were equivalent after matching patients with respect to presentation, procedure and demographic factors. Black–white 30-day mortality differences were equivalent after matching on procedure and demographic factors. Racial disparities in outcomes remained unchanged between the two time periods spanning 10 years. All patients in hospitals with better nurse resources had lower odds of 30-day (OR 0.60, 95% CI 0.46 to 0.78, pConclusionsSurvival disparities among black and white patients are largely explained by differences in demographic, procedure and presentation factors. Better nurse resources (eg, staffing, work environment) were associated with lower mortality for all patients.

A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.

Summary In an observational study matched for observed covariates, an association between treatment received and outcome exhibited may indicate not an effect caused by the treatment, but merely some bias in the allocation of treatments to individuals within matched pairs. The evidence that distinguishes moderate biases from causal effects is unevenly dispersed among possible comparisons in an observational study: some comparisons are insensitive to larger biases than others. Intuitively, larger treatment effects tend to be insensitive to larger unmeasured biases, and perhaps matched pairs can be grouped using covariates, doses or response patterns so that groups of pairs with larger treatment effects may be identified. Even if an investigator has a reasoned conjecture about where to look for insensitive comparisons, that conjecture might prove mistaken, or, when not mistaken, it might be received sceptically by other scientists who doubt the conjecture or judge it to be too convenient in light of its success with the data at hand. In this article a test is proposed that searches for insensitive findings over many comparisons, but controls the probability of falsely rejecting a true null hypothesis of no treatment effect in the presence of a bias of specified magnitude. An example is studied in which the test considers many comparisons and locates an interpretable comparison that is insensitive to larger biases than a conventional comparison based on Wilcoxon’s signed rank statistic applied to all pairs. A simulation examines the power of the proposed test. The method is implemented in the R package dstat, which contains the example and reproduces the analysis.

Absent randomization, causal conclusions gain strength if several independent evidence factors concur. We develop a method for constructing evidence factors from several instruments plus a direct c...

Summary We show that the strength of an instrument is incompletely characterized by the proportion of compliers, and we propose and evaluate new methods that extract more information from certain settings with comparatively few compliers. Specifically, we demonstrate that, for a fixed small proportion of compliers, the presence of an equal number of always-takers and never-takers weakens an instrument, whereas the absence of always-takers or, equivalently, the absence of never-takers strengthens an instrument. In this statement, the strength of an instrument refers to its ability to recognize and reject a false hypothesis about a structural parameter. Equivalently, the strength of an instrument refers to its ability to exclude from a confidence interval a false value of a structural parameter. This ability is measured by the Bahadur efficiency of a test that assumes that the instrument is flawless, or the Bahadur efficiency of a sensitivity analysis that assumes that the instrument may be somewhat biased. When there are few compliers, the outcomes for most people are unaffected by fluctuations in the instrument, so most of the information about the treatment effect is contained in the tail of the distribution of the outcomes. Exploiting this fact, we propose new methods that emphasize the affected portion of the distribution of outcomes, thereby extracting more information from studies with few compliers. Studies of the effects of Catholic high schools on academic test performance have used ‘being Catholic’ as an instrument for ‘attending a Catholic high school’, and the application concerns such a comparison using the US National Educational Longitudinal Study. Most Catholics did not attend Catholic school, so there are few compliers, but it was rare for non-Catholics to attend Catholic school, so there are very few always-takers.

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.

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.

To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics.Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear.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 ratios0.05).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.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 (P0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P0.0001), and paired cost difference = $3773 (P0.0001), 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.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.

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.

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 HDL cholesterol levels. In this study, the method quickly optimizes a nonlinear function of 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. This article is protected by copyright. All rights reserved.

Observational or nonrandomized studies of treatment effects are often constructed with the aid of polynomial-time algorithms that optimally form matched treatment-control pairs or matched sets. Bec...

We propose new optimal matching techniques for large administrative data sets. In current practice, very large matched samples are constructed by subdividing the population and solving a series of smaller problems, for instance, matching men to men and separately matching women to women. Without simplification of some kind, the time required to optimally match $T$ treated individuals to $T$ controls selected from $C\geq T$ potential controls grows much faster than linearly with the number of people to be matched—the required time is of order $O\{(T+C)^{3}\}$—so splitting one large problem into many small problems greatly accelerates the computations. This common practice has several disadvantages that we describe. In its place, we propose a single match, using everyone, that accelerates the computations in a different way. In particular, we use an iterative form of Glover’s algorithm for a doubly convex bipartite graph to determine an optimal caliper for the propensity score, radically reducing the number of candidate matches; then we optimally match in a large but much sparser graph. In this graph, a modified form of near-fine balance can be used on a much larger scale, improving its effectiveness. We illustrate the method using data from US Medicaid, matching children receiving surgery at a children’s hospital to similar children receiving surgery at a hospital that mostly treats adults. In the example, we form 38,841 matched pairs from 159,527 potential controls, controlling for 29 covariates plus 463 Principal Surgical Procedures, plus 973 Principal Diagnoses. The method is implemented in an $\mathtt{R}$ package $\mathtt{bigmatch}$ available from $\mathtt{CRAN}$.

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

BackgroundThere are known clinical benefits associated with investments in nursing. Less is known about their value.AimsTo compare surgical patient outcomes and costs in hospitals with better versus worse nursing resources and to determine if value differs across these hospitals for patients with different mortality risks.MethodsRetrospective matched-cohort design of patient outcomes at hospitals with better versus worse nursing resources, defined by patient-to-nurse ratios, skill mix, proportions of bachelors-degree nurses and nurse work environments. The sample included 62 715 pairs of surgical patients in 76 better nursing resourced hospitals and 230 worse nursing resourced hospitals from 2013 to 2015. Patients were exactly matched on principal procedures and their hospital’s size category, teaching and technology status, and were closely matched on comorbidities and other risk factors.ResultsPatients in hospitals with better nursing resources had lower 30-day mortality: 2.7% vs 3.1% (pConclusionHospitals with better nursing resources provided better clinical outcomes for surgical patients at a small additional cost. Generally, the sicker the patient, the greater the value at better nursing resourced hospitals.

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 (i) content validity, (ii) construct validity, and (iii) 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.