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We present a practical guide for the analysis of regression discontinuity (RD) designs in biomedical contexts. We begin by introducing key concepts, assumptions, and estimands within both the continuity‐based framework and the local randomization framework. We then discuss modern estimation and inference methods within both frameworks, including approaches for bandwidth or local neighborhood selection, optimal treatment effect point estimation, and robust bias‐corrected inference methods for uncertainty quantification. We also overview empirical falsification tests that can be used to support key assumptions. Our discussion focuses on two particular features that are relevant in biomedical research: (i) fuzzy RD designs, which often arise when therapeutic treatments are based on clinical guidelines, but patients with scores near the cutoff are treated contrary to the assignment rule; and (ii) RD designs with discrete scores, which are ubiquitous in biomedical applications. We illustrate our discussion with three empirical applications: the effect CD4 guidelines for anti‐retroviral therapy on retention of HIV patients in South Africa, the effect of genetic guidelines for chemotherapy on breast cancer recurrence in the United States, and the effects of age‐based patient cost‐sharing on healthcare utilization in Taiwan. Complete replication materials employing publicly available data and statistical software in Python, R and Stata are provided, offering researchers all necessary tools to conduct an RD analysis. [ABSTRACT FROM AUTHOR]

Abstract The method of difference-in-differences (DID) is widely used to study the causal effect of policy interventions in observational studies. DID employs a before and after comparison of the treated and control units to remove bias due to time-invariant unmeasured confounders under the parallel trends assumption. Estimates from DID, however, will be biased if the outcomes for the treated and control units evolve differently in the absence of treatment, namely if the parallel trends assumption is violated. We propose a general identification strategy that leverages two groups of control units whose outcomes relative to the treated units exhibit a negative correlation, and achieves partial identification of the average treatment effect for the treated. The identified set is of a union bounds form that involves the minimum and maximum operators, which makes the canonical bootstrap generally inconsistent and naive methods overly conservative. By using the directional inconsistency of the bootstrap distribution, we develop a novel bootstrap method to construct confidence intervals for the identified set and parameter of interest when the identified set is of a union bounds form, and we theoretically establish the asymptotic validity of the proposed method. We develop a simple falsification test and sensitivity analysis. We apply the proposed strategy for bracketing to study whether minimum wage laws affect employment levels. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract–In nonexperimental settings, the regression discontinuity (RD) design is one of the most credible identification strategies for program evaluation and causal inference. However, RD treatment effect estimands are necessarily local, making statistical methods for the extrapolation of these effects a key area for development. We introduce a new method for extrapolation of RD effects that relies on the presence of multiple cutoffs, and is therefore design-based. Our approach employs an easy-to-interpret identifying assumption that mimics the idea of "common trends" in difference-in-differences designs. We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility. for this article are available online. [ABSTRACT FROM AUTHOR]

When investigators seek to estimate causal effects, they often assume that selection into treatment is based only on observed covariates. Under this identification strategy, analysts must adjust for observed confounders. While basic regression models have long been the dominant method of statistical adjustment, methods based on matching or weighting have become more common. Of late, methods based on machine learning (ML) have been developed for statistical adjustment. These ML methods are often designed to be black box methods with little input from the researcher. In contrast, matching methods that use covariate prioritization are designed to allow for direct input from substantive investigators. In this article, we use a novel research design to compare matching with covariate prioritization to black box methods. We use black box methods to replicate results from five studies where matching with covariate prioritization was used to customize the statistical adjustment in direct response to substantive expertise. We compare the methods in terms of both point and interval estimation. We conclude with advice for investigators. [ABSTRACT FROM AUTHOR]

Southern states have used a variety of methods to disenfranchise African American voters. Empirical data on the effectiveness of these measures is rare. We present a unique data source from Louisiana that allows us to empirically document voter registration rates from the end of Reconstruction to the present. Using basic time series data, we document how voter registration rates changed over time in response to state restrictions. We then conduct a second analysis, which focuses on Louisiana's use of the Understanding Clause to reduce voter registration among Blacks. We show that in parishes that used the Understanding Clause, Black registration rates dropped by nearly 30 percentage points, with little effect on white registration. The findings of this paper have important implications for understanding the potential for discrimination in the enforcement of modern, ostensibly nonracial, voter eligibility requirements, such as voter ID laws, which grant substantial discretion to local officials in determining voter eligibility. [ABSTRACT FROM AUTHOR]

Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. Most IV analyses use naturally occurring instruments, such as distance to a hospital. In these analyses, investigators must assume that the instrument is as-if randomly assigned. This assumption cannot be tested directly, but it can be falsified. Most IV falsification tests compare relative prevalence or bias in observed covariates between the instrument and exposure. These tests require investigators to make covariate-by-covariate judgments about the validity of the IV design. Often, only some covariates are well-balanced, making it unclear whether as-if randomization can be assumed for the instrument. We propose an alternative falsification test that compares IV balance or bias with the balance or bias that would have been produced under randomization. A key advantage of our test is that it allows for global balance measures as well as easily interpretable graphical comparisons. Furthermore, our test does not rely on parametric assumptions and can be used to validly assess whether the instrument is significantly closer to being as-if randomized than the exposure. We demonstrate our approach using data from (SPOT)light, a prospective cohort study carried out in 48 National Health Service hospitals in the United Kingdom between November 1, 2010, and December 31, 2011. This study used bed availability in the intensive care unit as an instrument for admission to the intensive care unit. [ABSTRACT FROM AUTHOR]

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. We develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights, a generalization of inverse propensity score weights that solve a convex optimization problem to find a set of weights that directly minimize a measure of covariate imbalance, subject to an additional penalty on the variance of the weights. We tailor the approximate balancing weights optimization problem to the clustered observational study setting by deriving an upper bound on the mean square error and finding weights that minimize this upper bound, linking the level of covariate balance to a bound on the bias. We implement the procedure by specializing the bound to a random cluster‐level effects model, leading to a variance penalty that incorporates the signal‐to‐noise ratio and penalizes the weight on individuals and the total weight on groups differently according to the the intra‐class correlation. [ABSTRACT FROM AUTHOR]

The article focuses on the use of instrumental variables (IVs) in outcomes research. Topics discussed the variables highlight a number of ways to improve the quality of research that based on IV design, mentions the discussion on IV methods that are consistent with the results from randomized trials, and information on the empirical evidence for the IV design.

While randomization inference is well developed for continuous and binary outcomes, there has been comparatively little work for outcomes with nonnegative support and clumping at zero. Typically, outcomes of this type have been modeled using parametric models that impose strong distributional assumptions. This article proposes new randomization inference procedures for nonnegative outcomes with clumping at zero. Instead of making distributional assumptions, we propose various assumptions about the nature of the response to treatment and use permutation inference for both testing and estimation. This approach allows for some natural goodness-of-fit tests for model assessment, as well as flexibility in selecting test statistics sensitive to different potential alternatives. We illustrate our approach using two randomized trials, where job training interventions were designed to increase earnings of participants. [ABSTRACT FROM AUTHOR]

Confounding by indication is a critical challenge in evaluating the effectiveness of surgical interventions using observational data. The threat from confounding is compounded when using medical claims data due to the inability to measure risk severity. If there are unobserved differences in risk severity across patients, treatment effect estimates based on methods such a multivariate regression may be biased in an unknown direction. A research design based on instrumental variables offers one possibility for reducing bias from unobserved confounding compared to risk adjustment with observed confounders. This study investigates whether a physician's preference for operative care is a valid instrumental variable for studying the effect of emergency surgery. We review the plausibility of the necessary causal assumptions in an investigation of the effect of emergency general surgery (EGS) on inpatient mortality among adults using medical claims data from Florida, Pennsylvania, and New York in 2012–2013. In a departure from the extant literature, we use the framework of stochastic monotonicity which is more plausible in the context of a preference-based instrument. We compare estimates from an instrumental variables design to estimates from a design based on matching that assumes all confounders are observed. Estimates from matching show lower mortality rates for patients that undergo EGS compared to estimates based in the instrumental variables framework. Results vary substantially by condition type. We also present sensitivity analyses as well as bounds for the population level average treatment effect. We conclude with a discussion of the interpretation of estimates from both approaches. [ABSTRACT FROM AUTHOR]

We retrospectively studied the effect of introducing procalcitonin into clinical practice on antibiotic use within a large academic pediatric intensive care unit. In the absence of a standardized algorithm, availability of the procalcitonin assay did not reduce the frequency of antibiotic initiations or the continuation of antibiotics for greater than 72 hours. [ABSTRACT FROM AUTHOR]

What effect does a candidate's race have on coracial voter turnout? Recent studies have found mixed results, largely because it is difficult to separate the effect of candidate race from other factors that drive voter turnout. We argue that viability is a key element in the theory of turnout among coracial voters that has been overlooked in the extant literature. We develop a broad-based concept of candidate viability that is dependent on both the candidate and the electoral environment. To test this hypothesis, we make use of the unique runoff structure of mayoral elections in the state of Louisiana between 1988 and 2011.We argue that runoff elections heighten viability in ways rarely seen in most elections. We find that while there is an effect of candidate race on black turnout in general elections, the effect is much more robust in runoff elections. [ABSTRACT FROM AUTHOR]

A distinctive feature of a clustered observational study is its multilevel or nested data structure arising from the assignment of treatment, in a nonrandom manner, to groups or clusters of units or individuals. Examples are ubiquitous in the health and social sciences including patients in hospitals, employees in firms, and students in schools. What is the optimal matching strategy in a clustered observational study? At first thought, one might start by matching clusters of individuals and then, within matched clusters, continue by matching individuals. But as we discuss in this article, the optimal strategy is the opposite: in typical applications, where the intracluster correlation is not one, it is best to first match individuals and, once all possible combinations of matched individuals are known, then match clusters. In this article, we use dynamic and integer programming to implement this strategy and extend optimal matching methods to hierarchical and multilevel settings. Among other matched designs, our strategy can approximate a paired clustered randomized study by finding the largest sample of matched pairs of treated and control individuals within matched pairs of treated and control clusters that is balanced according to specifications given by the investigator. This strategy directly balances covariates both at the cluster and individual levels and does not require estimating the propensity score, although the propensity score can be balanced as an additional covariate. We illustrate our results with a case study of the comparative effectiveness of public versus private voucher schools in Chile, a question of intense policy debate in the country at the present. [ABSTRACT FROM AUTHOR]

In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no “gold standard” method for the identification of causal mediation effects. In particular, mediation effects will always have the character of estimates from observational data since they are generally subject to a specific form of confounding. Additionally, I demonstrate how randomization of the mediator and instrumental variables methods lead to fundamentally different quantities than causal mediation analyses. I also review recent work that discusses how the assumptions of mediation analyses differ when there is treatment noncompliance or when there are multiple mediators. Throughout, I motivate concepts using path diagrams and an example of a classroom intervention designed to increase test scores. [ABSTRACT FROM AUTHOR]

Scholars of state politics are often interested in the causal effects of legislative institutions on policy outcomes. For example, during the 1990s a number of states adopted term limits for state legislators. Advocates of term limits argued that this institutional reform would alter state policy in a number of ways, including limiting state expenditures. We highlight a number of research design issues that complicate attempts to estimate the effect of institutions on state outcomes by addressing the question of term limits and spending. In particular, we focus on (1) treatment effect heterogeneity and (2) the suitability of nonterm-limit states as good counterfactuals for term-limit states. We compare two different identification strategies to deal with these issues: differences-in-differences ( DID) estimation and conditioning on prior outcomes with an emphasis on synthetic case control. Using more rigorous methods of causal inference, we find little evidence that term limits affect state spending. Our analysis and results are informative for researchers seeking to assess the causal effects of state-level institutions. [ABSTRACT FROM AUTHOR]

Experiments have become an increasingly common tool for political science researchers over the last decade, particularly laboratory experiments performed on small convenience samples. We argue that the standard normal theory statistical paradigm used in political science fails to meet the needs of these experimenters and outline an alternative approach to statistical inference based on randomization of the treatment. The randomization inference approach not only provides direct estimation of the experimenter's quantity of interest-the certainty of the causal inference about the observed units-but also helps to deal with other challenges of small samples. We offer an introduction to the logic of randomization inference, a brief overview of its technical details, and guidance for political science experimenters about making analytic choices within the randomization inference framework. Finally, we reanalyze data from two political science experiments using randomization tests to illustrate the inferential differences that choosing a randomization inference approach can make. [ABSTRACT FROM AUTHOR]

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, the study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method for assessing the sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies. [ABSTRACT FROM PUBLISHER]

When will people become ambivalent about politics? One possibility is that the roots of ambivalence lie within the individual, with differences in political knowledge and attitude strength predicting whether a person internalizes the conflicts of politics. Alternately, attitudinal ambivalence could result from structural differences in the way political choices are presented in the wider political environment. We explore the degree to which different environments promote or limit ambivalence using a matching approach in conjunction with a set of multilevel models. We find that campaign environments can induce candidate ambivalence. In presidential elections, campaign efforts promote ambivalence most when competition between partisan campaign efforts is high. In House elections, campaign spending has a direct effect on levels of candidate ambivalence, where a candidate's spending decreases ambivalence about that candidate and increases ambivalence about opponents. [ABSTRACT FROM AUTHOR]

Dramatic world change has stimulated interest in research questions about the dynamics of politics. We have seen increases in the number of time series data sets and the length of typical time series. But three shortcomings are prevalent in published time series analysis. First, analysts often estimate models without testing restrictions implied by their specification. Second, researchers link the theoretical concept of equilibrium with cointegration and error correction models. Third, analysts often do a poor job of interpreting results. The consequences include weak connections between theory and tests, biased estimates, and incorrect inferences. We outline techniques for estimating linear dynamic regressions with stationary data and weakly exogenous regressors. We recommend analysts (1) start with general dynamic models and test restrictions before adopting a particular specification and (2) use the wide array of information available from dynamic specifications. We illustrate this strategy with data on Congressional approval and tax rates across OECD countries. [ABSTRACT FROM AUTHOR]

It is well understood that trust in government responds to the performance of the president, Congress, and the economy. Despite improved government performance, however, trust has never returned to the levels witnessed in the 1950s and 1960s. Social capital may be the force that has kept trust low. If so, we need to assess the relative contributions of both government performance and social capital at the macro level. Using macrolevel data, the analysis, here, is designed to capture the variation over time in both social capital and government performance and let them compete to explain the macro variation in trust. The empirical results demonstrate that both government performance and social capital matter, but that social capital appears to be the force which accounts for the decline in trust over the last 40 years. [ABSTRACT FROM AUTHOR]

Are some people more prone to instabilities in partisanship due to the ways they rank and organize their core values? We investigate the mechanisms of partisan volatility, considering whether instabilities reflect value conflict and ambivalence. Our expectation is that when the basic values of the American ethos come into conflict in elite discourse, citizens have difficulty reconciling their own value arrangement with that of elites, resulting in greater partisan volatility. To this end, we use several heteroscedastic regression and ordered probit models to explore whether the conflict of competing values explains the response variance and instability of individual-level partisanship and ideology over time. To construct measures of value conflict, we rely on data from the 1992, 1994 and 1996 American National Election Studies. We find that, while instabilities in partisan identification reflect low information for some, the competition of core values generates volatility in partisan affiliations for others. In deliberating the value tradeoffs of politics, people may be of two minds even about central beliefs such as party identification. [ABSTRACT FROM AUTHOR]

For some time, one line of research on trust in government has stressed that trust results from evaluations of the institutions of government. Here, trust reflects public sentiments toward the responsiveness of the political process. Another line of research has alternatively countered that trust tends to be a reflection of political leaders. But this research has been largely unable to demonstrate that the performance of authorities matters relative to evaluations of the political process. Changes in partisan control of Congress and the presidency, however, provides us with a natural experiment where authorities change while the political process is constant. Here, partisans should trust government more when their party controls Congress, the presidency, or both. I find that trust does respond to changes in partisan control of the Congress and presidency, which demonstrates that the effect of authorities matters relative to evaluations of the political process. [ABSTRACT FROM AUTHOR]

Background: Paraesophageal hernia repairs (PEHRs) have high rates of radiographic recurrence, with some patients requiring repeat operation. This study characterizes patients who underwent PEHR to identify the factors associated with postoperative symptom improvement and radiographic recurrence. We furthermore use propensity score matching to compare patients undergoing initial and reoperative PEHR to identify the factors predictive of recurrence or need for reoperation. Methods: After IRB approval, patients who underwent PEHR at a tertiary care center between January 2018 and December 2022 were identified. Patient characteristics, preoperative imaging, operative findings, and postoperative outcomes were recorded. A computational generalization of inverse propensity score weight was then used to construct populations of initial and redo PEHR patients with similar covariate distributions. Results: A total of 244 patients underwent PEHR (78.7% female, mean age 65.4 ± 12.3 years). Most repairs were performed with crural closure (81.4%) and fundoplication (71.7%) with 14.2% utilizing mesh. Postoperatively, 76.5% of patients had subjective symptom improvement and of 157 patients with postoperative imaging, 52.9% had evidence of radiographic recurrence at a mean follow-up of 10.4 ± 13.6 months. Only 4.9% of patients required a redo operation. Hernia type, crural closure, fundoplication, and mesh usage were not predictors of radiographic recurrence or symptom improvement (P > 0.05). Propensity weight score analysis of 50 redo PEHRs compared to a matched cohort of 194 initial operations revealed lower rates of postoperative symptom improvement (P < 0.05) but no differences in need for revision, complication rates, ED visits, or readmissions. Conclusions: Most PEHR patients have symptomatic improvement with minimal complications and reoperations despite frequent radiographic recurrence. Hernia type, crural closure, fundoplication, and mesh usage were not significantly associated with recurrence or symptom improvement. Compared to initial PEHR, reoperative PEHRs had lower rates of symptom improvement but similar rates of recurrence, complications, and need for reoperation. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has resulted in large-scale healthcare restrictions to control viral spread, reducing operating room censuses to include only medically necessary surgeries. The impact of restrictions on which patients undergo surgical procedures and their perioperative outcomes is less understood. Methods: Adult patients who underwent medically necessary surgical procedures at our institution during a restricted operative period due to the COVID-19 pandemic (March 23-April 24, 2020) were compared to patients undergoing procedures during a similar time period in the pre-COVID-19 era (March 25-April 26, 2019). Cardinal matching and differences in means were utilized to analyze perioperative outcomes. Results: 857 patients had surgery in 2019 (pre-COVID-19) and 212 patients had surgery in 2020 (COVID-19). The COVID-19 era cohort had a higher proportion of patients who were male (61.3% vs. 44.5%, P < 0.0001), were White (83.5% vs. 68.7%, P < 0.001), had private insurance (62.7% vs. 54.3%, p 0.05), were ASA classification 4 (10.9% vs. 3%, P < 0.0001), and underwent oncologic procedures (69.3% vs. 42.7%, P < 0.0001). Following 1:1 cardinal matching, COVID-19 era patients (N = 157) had a decreased likelihood of discharge to a nursing facility (risk difference-8.3, P < 0.0001) and shorter median length of stay (risk difference-0.6, p 0.04) compared to pre-COVID-19 era patients. There was no difference between the two patient cohorts in overall morbidity and 30-day readmission. Conclusions: COVID-19 restrictions on surgical operations were associated with a change in the racial and insurance demographics in patients undergoing medically necessary surgical procedures but were not associated with worse postoperative morbidity. Further study is necessary to better identify the causes for patient demographic differences. [ABSTRACT FROM AUTHOR]

Analyzing hospital-free days (HFDs) offers a patient-centered approach to health services research. We hypothesized that, within emergency general surgery (EGS), multimorbidity would be associated with fewer HFDs, whether patients were managed operatively or nonoperatively. EGS patients were identified using national Medicare claims data (2015-2018). Patients were classified as multimorbid based on the presence of a Qualifying Comorbidity Set and stratified by treatment: operative (received surgery within 48 h of index admission) and nonoperative. HFDs were calculated through 180 d, beginning on the day of index admission, as days alive and spent outside of a hospital, an Emergency Department, or a long-term acute care facility. Univariate comparisons were performed using Kruskal–Wallis tests and risk-adjusted HFDs were compared between multimorbid and nonmultimorbid patients using multivariable zero-inflated negative binomial regression models. Among 174,891 operative patients, 45.5% were multimorbid. Among 398,756 nonoperative patients, 59.2% were multimorbid. Multimorbid patients had fewer median HFDs than nonmultimorbid patients among operative and nonoperative cohorts (P < 0.001). At 6 mo, among operative patients, multimorbid patients had 6.5 fewer HFDs (P < 0.001), and among nonoperative patients, multimorbid patients had 7.9 fewer HFDs (P < 0.001). When length of stay was included as a covariate, nonoperative multimorbid patients still had 7.9 fewer HFDs than nonoperative, nonmultimorbid patients (P < 0.001). HFDs offer a patient-centered, composite outcome for claims-based analyses. For EGS patients, multimorbidity was associated with less time alive and out of the hospital, especially when patients were managed nonoperatively. • Hospital-free days (HFDs) offer a patient-centered approach to health services research. • HFDs are spent outside of a hospital, long-term care facility, or an ED. • Multimorbidity relates to specific combinations of comorbid conditions. • Multimorbidity is associated with fewer HFDs among emergency general surgery patients. • The difference in HFDs by multimorbidity increases over time. [Display omitted] [ABSTRACT FROM AUTHOR]

Prior studies have sought to describe Emergency General Surgery (EGS) burden, but a detailed description of resource utilization for both operative and nonoperative management of EGS conditions has not been undertaken. Patient and hospital characteristics were extracted from Medicare data, 2015-2018. Operations, nonsurgical procedures, and other resources (i.e., radiology) were defined using Current Procedural Terminology codes. One million eight hundred two thousand five hundred forty-five patients were included in the cohort. The mean age was 74.7 y and the most common diagnoses were upper gastrointestinal. The majority of hospitals were metropolitan (75.1%). Therapeutic radiology services were available in 78.4% of hospitals and operating rooms or endoscopy suites were available in 92.5% of hospitals. There was variability in resource utilization across EGS subconditions, with hepatobiliary (26.4%) and obstruction (23.9%) patients most frequently undergoing operation. Treatment of EGS diseases in older adults involves several interventional resources. Changes in EGS models, acute care surgery training, and interhospital care coordination may be beneficial to the treatment of EGS patients. [ABSTRACT FROM AUTHOR]

Pretreatment selection or censoring ("selection on treatment") can occur when two treatment levels are compared ignoring the third option of neither treatment, in "censoring by death" settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

BACKGROUND: Little is known about the impact of multimorbidity on long-term outcomes for older emergency general surgery patients. STUDY DESIGN: Medicare beneficiaries, age 65 and older, who underwent operative management of an emergency general surgery condition were identified using Centers for Medicare & Medicaid claims data. Patients were classified as multimorbid based on the presence of a Qualifying Comorbidity Set (a specific combination of comorbid conditions known to be associated with increased risk of in-hospital mortality in the general surgery setting) and compared with those without multimorbidity. Risk-adjusted outcomes through 180 days after discharge from index hospitalization were calculated using linear and logistic regressions. RESULTS: Of 174,891 included patients, 45.5% were identified as multimorbid. Multimorbid patients had higher rates of mortality during index hospitalization (5.9% vs 0.7%, odds ratio [OR] 3.05, p < 0.001) and through 6 months (17.1% vs 3.4%, OR 2.33, p < 0.001) after discharge. Multimorbid patients experienced higher rates of readmission at 1 month (22.9% vs 11.4%, OR 1.48, p < 0.001) and 6 months (38.2% vs 21.2%, OR 1.48, p < 0.001) after discharge, lower rates of discharge to home (42.5% vs 74.2%, OR 0.52, p < 0.001), higher rates of discharge to rehabilitation/nursing facility (28.3% vs 11.3%, OR 1.62, p < 0.001), greater than double the use of home oxygen, walker, wheelchair, bedside commode, and hospital bed (p < 0.001), longer length of index hospitalization (1.33 additional in-patient days, p < 0.001), and higher costs through 6 months ($5,162 additional, p < 0.001). CONCLUSIONS: Older, multimorbid patients experience worse outcomes, including survival and independent function, after emergency general surgery than nonmultimorbid patients through 6 months after discharge from index hospitalization. This information is important for setting recovery expectations for high-risk patients to improve shared decision-making. [ABSTRACT FROM AUTHOR]

In some observational studies of treatment effects, matched samples are created so treated and control groups are similar in terms of observable covariates. Traditionally, such matched samples consist of matched pairs. However, alternative forms of matching may have desirable features. One strategy that may improve efficiency is to match a variable number of control units to each treated unit. Another strategy to improve balance is to adopt a fine balance constraint. Under a fine balance constraint, a nominal covariate is exactly balanced, but it does not require individually matched treated and control subjects for this variable. Here, we propose a method to allow for fine balance constraints when each treated unit is matched to a variable number of control units, which is not currently possible using existing matching network flow algorithms. Our approach uses the entire number to first determine the optimal number of controls for each treated unit. For each stratum of matched treated units, we can then apply a fine balance constraint. We then demonstrate that a matched sample for the evaluation of the Peer Health Exchange, an intervention in schools designed to decrease risky health behaviors among youths, using a variable number of controls and fine balance constraint is superior to simply using a variable-ratio match. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]