Effect of Gamification, Financial Incentives, or Both to Increase Physical Activity Among Patients at High Risk of Cardiovascular Events: The BE ACTIVE Randomized Controlled Trial.

BACKGROUND: Physical activity is associated with a lower risk of major adverse cardiovascular events, but few individuals achieve guideline-recommended levels of physical activity. Strategies informed by behavioral economics increase physical activity, but their longer-term effectiveness is uncertain. We sought to determine the effect of behaviorally designed gamification, loss-framed financial incentives, or their combination on physical activity compared with attention control over 12-month intervention and 6-month postintervention follow-up periods. METHODS: Between May 2019 and January 2024, participants with clinical atherosclerotic cardiovascular disease or a 10-year risk of myocardial infarction, stroke, or cardiovascular death of ≥7.5% by the Pooled Cohort equation were enrolled in a pragmatic randomized clinical trial. Participants received a wearable device to track daily steps, established a baseline, selected a step goal increase, and were randomly assigned to control (n=151), behaviorally designed gamification (n=304), loss-framed financial incentives (n=302), or gamification+financial incentives (n=305). The primary outcome of the trial was the change in mean daily steps from baseline through the 12-month intervention period. RESULTS: A total of 1062 patients (mean±SD age, 67±8; 61% female; 31% non-White) were enrolled. Compared with control subjects, participants had significantly greater increases in mean daily steps from baseline during the 12-month intervention in the gamification arm (adjusted difference, 538.0 [95% CI, 186.2-889.9]; P=0.0027), financial incentives arm (adjusted difference, 491.8 [95% CI, 139.6-844.1]; P=0.0062), and gamification+financial incentives arm (adjusted difference, 868.0 [95% CI, 516.3-1219.7]; P<0.0001). During the 6-month follow-up, physical activity remained significantly greater in the gamification+financial incentives arm than in the control arm (adjusted difference, 576.2 [95% CI, 198.5-954]; P=0.0028), but it was not significantly greater in the gamification (adjusted difference, 459.8 [95% CI, 82.0-837.6]; P=0.0171) or financial incentives (adjusted difference, 327.9 [95% CI, -50.2 to 706]; P=0.09) arms after adjustment for multiple comparisons. CONCLUSIONS: Behaviorally designed gamification, loss-framed financial incentives, and the combination of both increased physical activity compared with control over a 12-month intervention period, with the largest effect in gamification+financial incentives. These interventions could be a useful component of strategies to reduce cardiovascular risk in high-risk patients. REGISTRATION: URL: https://clinicaltrials.gov; Unique Identifier: NCT03911141.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials.

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients: A Pragmatic Cluster Randomized Trial.

Importance: Increasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking. Objective: To determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes. Design, Setting, and Participants: A pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019. Intervention: Ordering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care. Main Outcomes and Measures: The primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality. Results: Of 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P < .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, -0.53% [95% CI, -3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]). Conclusions and Relevance: Default palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes. Trial Registration: ClinicalTrials.gov Identifier: NCT02505035.

Sensitivity Analysis for Observational Studies with Recurrent Events.

We conduct an observational study of the effect of sickle cell trait Haemoglobin AS (HbAS) on the hazard rate of malaria fevers in children. Assuming no unmeasured confounding, there is strong evidence that HbAS reduces the rate of malarial fevers. Since this is an observational study, however, the no unmeasured confounding assumption is strong. A sensitivity analysis considers how robust a conclusion is to a potential unmeasured confounder. We propose a new sensitivity analysis method for recurrent event data and apply it to the malaria study. We find that for the causal conclusion that HbAS is protective against malarial fevers to be overturned, the hypothesized unmeasured confounder must be as influential as all but one of the measured confounders.

A randomized controlled trial of gamification, financial incentives, or both to increase physical activity among patients with elevated risk for cardiovascular disease: rationale and design of the be active study.

BACKGROUND: Higher levels of physical activity are associated with improvements in cardiovascular health, and consensus guidelines recommend that individuals with or at risk for atherosclerotic cardiovascular disease (ASCVD) participate in regular physical activity. However, most adults do not achieve recommended levels of physical activity. Concepts from behavioral economics have been used to design scalable interventions that increase physical activity over short time periods, but the longer-term efficacy of these strategies is uncertain. STUDY DESIGN AND OBJECTIVES: BE ACTIVE (NCT03911141) is a pragmatic, virtual, randomized controlled trial designed to evaluate the effectiveness of 3 strategies informed by behavioral economic concepts to increase daily physical activity in patients with established ASCVD or 10-year ASCVD risk > 7.5% who are seen in primary care and cardiology clinics affiliated with the University of Pennsylvania Health System. Patients are contacted by email or text message, and complete enrollment and informed consent on the Penn Way to Health online platform. Patients are then provided with a wearable fitness tracker, establish a baseline daily step count, set a goal to increase daily step count by 33% to 50%, and are randomized 1:2:2:2 to control, gamification, financial incentives, or both gamification and financial incentives. Interventions continue for 12 months, with follow-up for an additional 6 months to evaluate the durability of behavior change. The trial has met its enrollment goal of 1050 participants, with a primary endpoint of change from baseline in daily steps over the 12-month intervention period. Key secondary endpoints include change from baseline in daily steps over the 6-month post-intervention follow-up period and change in moderate to vigorous physical activity over the intervention and follow-up periods. If the interventions prove effective, their effects on life expectancy will be compared with their costs in cost-effectiveness analysis. CONCLUSIONS: BE ACTIVE is a virtual, pragmatic randomized clinical trial powered to demonstrate whether gamification, financial incentives, or both are superior to attention control in increasing physical activity. Its results will have important implications for strategies to promote physical activity in patients with or at risk for ASCVD, as well as for the design and implementation of pragmatic virtual clinical trials within health systems.

Instrumented difference-in-differences.

Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variables and difference-in-differences, we propose a new method called instrumented difference-in-differences that explicitly leverages exogenous randomness in an exposure trend to estimate the average and conditional average treatment effect in the presence of unmeasured confounding. We develop the identification assumptions using the potential outcomes framework. We propose a Wald estimator and a class of multiply robust and efficient semiparametric estimators, with provable consistency and asymptotic normality. In addition, we extend the instrumented difference-in-differences to a two-sample design to facilitate investigations of delayed treatment effect and provide a measure of weak identification. We demonstrate our results in simulated and real datasets.

Rejoinder to "Instrumented difference-in-differences".

We thank all the discussants for the careful reading and insightful comments. In our rejoinder, we extend the discussion of how the assumptions of instrumented difference-in-differences (iDID) compare to the assumptions of the standard instrumental variable method. We also make additional comments on how iDID is related to the fuzzy DID. We highlight future research directions to enhance the utility of iDID, including extensions to adjust for covariate shift in two-sample iDID design, and generalization of iDID to multiple time points and a multi-valued instrumental variable for DID.

Automated analysis of low-field brain MRI in cerebral malaria.

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.

Association of ICU Admission and Outcomes in Sepsis and Acute Respiratory Failure.

Rationale: Many decisions to admit patients to the ICU are not grounded in evidence regarding who benefits from such triage, straining ICU capacity and limiting its cost-effectiveness. Objectives: To measure the benefits of ICU admission for patients with sepsis or acute respiratory failure. Methods: At 27 United States hospitals across two health systems from 2013 to 2018, we performed a retrospective cohort study using two-stage instrumental variable quantile regression with a strong instrument (hospital capacity strain) governing ICU versus ward admission among high-acuity patients (i.e., laboratory-based acute physiology score v2 ⩾ 100) with sepsis and/or acute respiratory failure who did not require mechanical ventilation or vasopressors in the emergency department. Measurements and Main Results: Among patients with sepsis (n = 90,150), admission to the ICU was associated with a 1.32-day longer hospital length of stay (95% confidence interval [CI], 1.01-1.63; P < 0.001) (when treating deaths as equivalent to long lengths of stay) and higher in-hospital mortality (odds ratio, 1.48; 95% CI, 1.13-1.88; P = 0.004). Among patients with respiratory failure (n = 45,339), admission to the ICU was associated with a 0.82-day shorter hospital length of stay (95% CI, -1.17 to -0.46; P < 0.001) and reduced in-hospital mortality (odds ratio, 0.75; 95% CI, 0.57-0.96; P = 0.04). In sensitivity analyses of length of stay, excluding, ignoring, or censoring death, results were similar in sepsis but not in respiratory failure. In subgroup analyses, harms of ICU admission for patients with sepsis were concentrated among older patients and those with fewer comorbidities, and the benefits of ICU admission for patients with respiratory failure were concentrated among older patients, highest-acuity patients, and those with more comorbidities. Conclusions: Among high-acuity patients with sepsis who did not require life support in the emergency department, initial admission to the ward, compared with the ICU, was associated with shorter length of stay and improved survival, whereas among patients with acute respiratory failure, triage to the ICU compared with the ward was associated with improved survival.

Patient Safety Outcomes under Flexible and Standard Resident Duty-Hour Rules.

BACKGROUND: Concern persists that extended shifts in medical residency programs may adversely affect patient safety. METHODS: We conducted a cluster-randomized noninferiority trial in 63 internal-medicine residency programs during the 2015-2016 academic year. Programs underwent randomization to a group with standard duty hours, as adopted by the Accreditation Council for Graduate Medical Education (ACGME) in July 2011, or to a group with more flexible duty-hour rules that did not specify limits on shift length or mandatory time off between shifts. The primary outcome for each program was the change in unadjusted 30-day mortality from the pretrial year to the trial year, as ascertained from Medicare claims. We hypothesized that the change in 30-day mortality in the flexible programs would not be worse than the change in the standard programs (difference-in-difference analysis) by more than 1 percentage point (noninferiority margin). Secondary outcomes were changes in five other patient safety measures and risk-adjusted outcomes for all measures. RESULTS: The change in 30-day mortality (primary outcome) among the patients in the flexible programs (12.5% in the trial year vs. 12.6% in the pretrial year) was noninferior to that in the standard programs (12.2% in the trial year vs. 12.7% in the pretrial year). The test for noninferiority was significant (P = 0.03), with an estimate of the upper limit of the one-sided 95% confidence interval (0.93%) for a between-group difference in the change in mortality that was less than the prespecified noninferiority margin of 1 percentage point. Differences in changes between the flexible programs and the standard programs in the unadjusted rate of readmission at 7 days, patient safety indicators, and Medicare payments were also below 1 percentage point; the noninferiority criterion was not met for 30-day readmissions or prolonged length of hospital stay. Risk-adjusted measures generally showed similar findings. CONCLUSIONS: Allowing program directors flexibility in adjusting duty-hour schedules for trainees did not adversely affect 30-day mortality or several other measured outcomes of patient safety. (Funded by the National Heart, Lung, and Blood Institute and Accreditation Council for Graduate Medical Education; iCOMPARE ClinicalTrials.gov number, NCT02274818.).

Sleep and Alertness in a Duty-Hour Flexibility Trial in Internal Medicine.

BACKGROUND: A purpose of duty-hour regulations is to reduce sleep deprivation in medical trainees, but their effects on sleep, sleepiness, and alertness are largely unknown. METHODS: We randomly assigned 63 internal-medicine residency programs in the United States to follow either standard 2011 duty-hour policies or flexible policies that maintained an 80-hour workweek without limits on shift length or mandatory time off between shifts. Sleep duration and morning sleepiness and alertness were compared between the two groups by means of a noninferiority design, with outcome measures including sleep duration measured with actigraphy, the Karolinska Sleepiness Scale (with scores ranging from 1 [extremely alert] to 9 [extremely sleepy, fighting sleep]), and a brief computerized Psychomotor Vigilance Test (PVT-B), with long response times (lapses) indicating reduced alertness. RESULTS: Data were obtained over a period of 14 days for 205 interns at six flexible programs and 193 interns at six standard programs. The average sleep time per 24 hours was 6.85 hours (95% confidence interval [CI], 6.61 to 7.10) among those in flexible programs and 7.03 hours (95% CI, 6.78 to 7.27) among those in standard programs. Sleep duration in flexible programs was noninferior to that in standard programs (between-group difference, -0.17 hours per 24 hours; one-sided lower limit of the 95% confidence interval, -0.45 hours; noninferiority margin, -0.5 hours; P = 0.02 for noninferiority), as was the score on the Karolinska Sleepiness Scale (between-group difference, 0.12 points; one-sided upper limit of the 95% confidence interval, 0.31 points; noninferiority margin, 1 point; P<0.001). Noninferiority was not established for alertness according to the PVT-B (between-group difference, -0.3 lapses; one-sided upper limit of the 95% confidence interval, 1.6 lapses; noninferiority margin, 1 lapse; P = 0.10). CONCLUSIONS: This noninferiority trial showed no more chronic sleep loss or sleepiness across trial days among interns in flexible programs than among those in standard programs. Noninferiority of the flexible group for alertness was not established. (Funded by the National Heart, Lung, and Blood Institute and American Council for Graduate Medical Education; ClinicalTrials.gov number, NCT02274818.).

New multivariate tests for assessing covariate balance in matched observational studies.

We propose new tests for assessing whether covariates in a treatment group and matched control group are balanced in observational studies. The tests exhibit high power under a wide range of multivariate alternatives, some of which existing tests have little power for. The asymptotic permutation null distributions of the proposed tests are studied and the P-values calculated through the asymptotic results work well in simulation studies, facilitating the application of the test to large data sets. The tests are illustrated in a study of the effect of smoking on blood lead levels. The proposed tests are implemented in an R package BalanceCheck.

Two robust tools for inference about causal effects with invalid instruments.

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well-known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals outperform traditional instrumental variable confidence intervals when invalid instruments are present and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low-density lipoprotein on globulin levels.

Delayed presentation to hospital care is associated with sequelae but not mortality in children with cerebral malaria in Malawi.

BACKGROUND: Cerebral malaria is still a major cause of death in children in sub-Saharan Africa. Among survivors, debilitating neurological sequelae can leave children with permanent cognitive impairments and societal stigma, resulting in taxing repercussions for their families. This study investigated the effect of delay in presentation to medical care on outcome in children with cerebral malaria in Malawi. METHODS: This retrospective study included participants enrolled in a longstanding study of cerebral malaria between 2001 and 2021 and considered coma duration prior to arrival at hospital (with or without anti-malarial treatment), HIV status, blood lactate levels at admission and age as factors that could affect clinical outcome. Outcomes were categorized as full recovery, sequelae at the time of discharge, or death. A multinomial regression was fit and run controlling for coma duration, HIV status, lactate levels and age, to determine the association between each explanatory variable and outcome. RESULTS: A total of 1663 children with cerebral malaria, aged 6 months to 14 years were included. Longer coma duration (in hours) was associated with greater odds of developing sequelae (OR = 1.023, 95% CI 1.007-1.039, p = 0.006) but not death (OR = 1.00, 95% CI 0.986-1.015, p = 0.961). Younger age (in months) was also correlated with higher rates of sequelae, (OR = 0.990, 95% CI 0.983-0.997, p = 0.004) but not with increased mortality (OR = 0.998, 95% CI 0.993-1.003, p = 0.335). Blood lactate levels on admission were correlated with mortality (OR = 1.125, 95% CI 1.090-1.161, p < 0.001) but not associated with increased rates of sequelae (OR = 1.016, 95% CI 0.973-1.060, p = 0.475). Positive HIV status and treatment with an anti-malarial (artemisinin or non-artemisinin-based) prior to arrival at the hospital were not significantly associated with either adverse outcome. CONCLUSIONS: In Malawian children with cerebral malaria, higher rates of sequelae were significantly associated with extended coma duration prior to admission and younger age. Mortality rates were correlated with increased lactate levels on admission. The differential effects of variables on clinical outcomes suggest that there may be different pathogenic pathways leading to sequelae and death. Actions taken by parents and health care professionals are critical in defining when patients arrive at hospital and determining their ultimate outcome.

Equitable implementation of S.A.F.E. Firearm: A multi-method pilot study.

Attention to health equity is critical in the implementation of firearm safety efforts. We present our operationalization of equity-oriented recommendations in preparation for launch of a hybrid effectiveness-implementation trial focused on firearm safety promotion in pediatric primary care as a universal suicide prevention strategy. In Step 1 of our process, pre-trial engagement with clinican partners and literature review alerted us that delivery of a firearm safety program may vary by patients' medical complexity, race, and ethnicity. In Step 2, we selected the Health Equity Implementation Framework to inform our understanding of contextual determinants (i.e., barriers and facilitators). In Step 3, we leveraged an implementation pilot across 5 pediatric primary care clinics in 2 health system sites to study signals of inequities. Eligible well-child visits for 694 patients and 47 clinicians were included. Our results suggested that medical complexity was not associated with program delivery. We did see potential signals of inequities by race and ethnicity but must interpret with caution. Though we did not initially plan to examine differences by sex assigned at birth, we discovered that clinicians may be more likely to deliver the program to parents of male than female patients. Seven qualitative interviews with clinicians provided additional context. In Step 4, we interrogated equity considerations (e.g., why and how do these inequities exist). In Step 5, we will develop a plan to probe potential inequities related to race, ethnicity, and sex in the fully powered trial. Our process highlights that prospective, rigorous, exploratory work is vital for equity-informed implementation trials.

Selective inference for effect modification via the lasso.

Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the lasso to choose a low-complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method.

Hospital Readmission and Post-Acute Care Use After Intensive Care Unit Admissions: New ICU Quality Metrics?

OBJECTIVE: Care coordination is a national priority. Post-acute care use and hospital readmission appear to be common after critical illness. It is unknown whether specialty critical care units have different readmission rates and what these trends have been over time. METHODS: In this retrospective cohort study, a cohort of 53,539 medical/surgical patients who were treated in a critical care unit during their index admission were compared with 209,686 patients who were not treated in a critical care unit. The primary outcome was 30-day all cause hospital readmission. Secondary outcomes included post-acute care resource use and immediate readmission, defined as within 7 days of discharge. RESULTS: Compared to patients discharged after an index hospitalization without critical illness, surviving patients following ICU admission were not more likely to be rehospitalized within 30 days (15.8 vs. 16.1%, p = 0.08). However, they were more likely to receive post-acute care services (45.3% vs. 70.9%, p < 0.001) as well as be rehospitalized within 7 days (5.2 vs. 6.0%, p < 0.001). Post-acute care use and 30-day readmission rates varied by ICU type, the latter ranging from 11.7% after admission in a cardiothoracic critical care unit to 23.1% after admission in a medical critical care unit. 30-day readmission after ICU admission did not decline between 2010 and 2015 (p = 0.38). Readmission rates declined over time for 2 of 4 targeted conditions (heart failure and chronic obstructive pulmonary disease), but only when the hospitalization did not include ICU admission. CONCLUSIONS: Rehospitalization for survivors following ICU admission is common across all specialty critical care units. Post-acute care use is also common for this population of patients. Overall trends for readmission rates after critical illness did not change over time, and readmission reductions for targeted conditions were limited to hospitalizations that did not include an ICU admission.

Estimating Unhealthy Food Effects on Childhood Overweight in Malawi Using an Observational Study.

INTRODUCTION: Consumption of unhealthy foods in children contributes to high levels of childhood obesity globally. In developing countries there is paucity of empirical studies on the association.  This study employed propensity-score methods to evaluate the effect of unhealthy foods on overweight among children in Malawi using observational data. METHODS: Data on 4625 children aged 6 to 59 months from the 2015-16 Malawi Demographic and Health Survey (MDHS) were analyzed. A multivariable logistic regression model of unhealthy foods (yes or no) on purported confounders of childhood overweight was used to obtain a child's unhealthy food propensity score. The propensity scores were then used to form matched sets of healthy and unhealthy fed children. The association between unhealthy foods and childhood overweight was assessed using the conditional logistic regression model. RESULTS: The prevalence of overweight (body mass index (BMI) z-score > 2 standard deviations) was estimated at 4.5% (3.8%, 5.3%). The proportion of children who consumed unhealthy foods was estimated at 14.6% (95% CI: 13.1%, 16.2%). Our propensity score matching achieved a balance in the distribution of the confounders between children in the healthy and unhealthy food groups. Children fed unhealthy foods were significantly more likely to be overweight than those fed healthy foods (OR = 2.5, 95% CI: (1.2, 5.2)). CONCLUSION: The findings suggest the adverse effects of unhealthy foods on childhood overweight in Malawi. Thus, efforts to reduce unhealthy food consumption among children should be implemented and supported to address the problem of childhood overweight in Malawi and the sub-Saharan African region.