Publications

2024
Omar AV Mejia, Gabrielle B Borgomoni, Fabiane Letícia de Freitas, Lucas S Furlán, Bianca Maria M Orlandi, Marcos G Tiveron, Pedro Gabriel MBE de Silva, Marcelo A Nakazone, Marco Antonio P de Oliveira, Valquíria P Campagnucci, Sharon-Lise Normand, Roger D Dias, and Fábio B Jatene. 2024. “Data-driven coaching to improve statewide outcomes in CABG: before and after interventional study.” Int J Surg.Abstract
BACKGROUND: The impact of quality improvement initiatives Program (QIP) on coronary artery bypass grafting surgery (CABG) remains scarce, despite improved outcomes in other surgical areas. This study aims to evaluate the impact of a package of QIP on mortality rates among patients undergoing CABG. MATERIALS AND METHODS: This prospective cohort study utilized data from the multicenter database Registro Paulista de Cirurgia Cardiovascular II (REPLICCAR II), spanning from July 2017 to June 2019. Data from 4,018 isolated CABG adult patients were collected and analyzed in three phases: before-implementation, implementation, and after-implementation of the intervention (which comprised QIP training for the hospital team). Propensity Score Matching was used to balance the groups of 2,170 patients each for a comparative analysis of the following outcomes: reoperation, deep sternal wound infection/mediastinitis ≤ 30 days, cerebrovascular accident, acute kidney injury, ventilation time>24 hours, length of stay<6 days, length of stay>14 days, morbidity and mortality, and operative mortality. A multiple regression model was constructed to predict mortality outcomes. RESULTS: Following implementation, there was a significant reduction of operative mortality (61.7%, P=0.046), as well as deep sternal wound infection/mediastinitis (P<0.001), sepsis (P=0.002), ventilation time in hours (P<0.001), prolonged ventilation time (P=0.009), postoperative peak blood glucose (P<0.001), total length of hospital stay (P<0.001). Additionally, there was a greater use of arterial grafts, including internal thoracic (P<0.001) and radial (P=0.038), along with a higher rate of skeletonized dissection of the internal thoracic artery. CONCLUSIONS: QIP was associated with a 61.7% reduction in operative mortality following CABG. Although not all complications exhibited a decline, the reduction in mortality suggests a possible decrease in failure to rescue during the after-implementation period.
Corey C Hardin, Michael Fralick, Daniel Muller, Kimberly Knoper, Alison Burke, Kathy Stern, Suellen Li, Sharon-Lise Normand, and Chana A Sacks. 2024. “How Treatment Effect Heterogeneity Works.” NEJM Evid, 3, 3, Pp. EVIDstat2400019.Abstract
How Treatment Effect Heterogeneity WorksThis Stats, STAT! animated video explores the concept of treatment effect heterogeneity. Differences in the effectiveness of treatments across participants in a clinical trial is important to understand when deciding how to apply clinical trial results to clinical practice.
Sung Eun Choi, Ankur Pandya, Joel White, Elizabeth Mertz, and Sharon-Lise Normand. 2024. “Quality Measure Adherence and Oral Health Outcomes in Children.” JAMA Netw Open, 7, 1, Pp. e2353861.Abstract
IMPORTANCE: Process-based quality measures are generally intended to promote evidence-based practices that have been proven to improve outcomes. However, due to lack of standardized implementation of diagnostic codes in dentistry, assessing the association between process and oral health outcomes has been challenging. OBJECTIVE: To estimate the association of adhering to dental quality measures with patient oral health outcomes. DESIGN, SETTING, AND PARTICIPANTS: Using a target trial emulation, a causal inference framework, this retrospective cohort study estimated the difference in the risk of developing tooth decay between US children who adhered to process-based dental quality measures (receiving topical fluoride and sealant [treated groups]) and those who did not (control groups). Electronic health records of US children and adolescents aged 0 to 18 years from January 1, 2014, to December 31, 2020, were used. To emulate random treatment assignment based on baseline confounders, coarsened exact matching was used to produce covariate balance between the treated and control groups. A time-to-event regression model produced effect estimates, adjusting for time-varying covariates. Near-far matching was used to account for unmeasured confounders as a sensitivity analysis. Data were analyzed from May 1 to August 7, 2023. EXPOSURES: Adherence to dental quality measures. MAIN OUTCOMES AND MEASURES: Incidence of tooth decay. RESULTS: Among 69 212 US children aged between 0 and 18 years (mean [SD] age, 10.2 [5.0] years; 49.5% male, 50.4% female, and 0.1% unknown or transgender), 1930 (2.8%) were Asian, 2038 (2.9%) were Black, 8667 (12.5%) were Hispanic, 33 632 (48.6%) were White, and 22 945 (33.2%) were multiracial, other, or missing racial and ethnic group identification. Relative to control individuals, treated individuals were more likely to be at elevated risk of caries (fluoride measure: 16 453 [76.5%] vs 15 236 [39.8%]; sealant measure: 2264 [54.6%] vs 997 [44.0%]) and have regular dental visits (fluoride measure: 21 498 [100%] vs 13 741 [35.9%]; sealant measure: 1623 [39.2%] vs 871 [38.4%]). Adherence to quality measures was associated with reduced risk of tooth decay with adjusted hazard ratios of 0.82 (95% CI, 0.78- 0.86) for fluoride and 0.86 (95% CI, 0.76-0.97) for sealant in the matched cohort. Benefits of adhering to quality measures were greater among children at elevated vs low risk and with public vs commercial insurance for both measures. CONCLUSIONS: In this cohort study, adhering to dental quality measures was associated with reduced risk of tooth decay, and benefits were greater among children at elevated risk and with public insurance. These findings provide insights in facilitating targeted application of quality measures or developing more tailored quality improvement initiatives.
Sharon-Lise T Normand. 2024. “Questioning a Sensible Result.” NEJM Evid, 3, 3, Pp. EVIDe2300324.Abstract
Contemporary data collection strategies, storage capabilities, and modern statistical methodology have made retrospective analyses of observational databases commonplace. Such databases afford opportunities to learn about the effectiveness and risks of interventions or health behaviors that generally cannot be randomized. In this issue of NEJM Evidence, Cho et al.1 assemble survey data and cohort data from four countries to quantify the association between age-sex-specific smoking cessation and mortality. The authors conclude that smoking cessation at any age is associated with lower excess overall mortality risk and lower death from diseases made more common by smoking. It is difficult to argue with this conclusion - to question the magnitude of the associations is not.
Jason Poulos, Marcela Horvitz-Lennon, Katya Zelevinsky, Tudor Cristea-Platon, Thomas Huijskens, Pooja Tyagi, Jiaju Yan, Jordi Diaz, and Sharon-Lise Normand. 2024. “Targeted learning in observational studies with multi-valued treatments: An evaluation of antipsychotic drug treatment safety.” Stat Med, 43, 8, Pp. 1489-1508.Abstract
We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39 000 adults with serious mental illnesses. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on 3-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug known for having low cardiometabolic risk.
2023
Sung Eun Choi, Joel White, Elizabeth Mertz, and Sharon-Lise Normand. 2023. “Analysis of Race and Ethnicity, Socioeconomic Factors, and Tooth Decay Among US Children.” JAMA Netw Open, 6, 6, Pp. e2318425.Abstract
IMPORTANCE: While large oral health disparities remain by race and ethnicity among children, the associations of race, ethnicity, and mediating factors with oral health outcomes are poorly characterized. Identifying the pathways that explain these disparities would be critical to inform policies to effectively reduce them. OBJECTIVE: To measure racial and ethnic disparities in the risk of developing tooth decay and quantify relative contributions of factors mediating the observed disparities among US children. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used electronic health records of US children from 2014 to 2020 to measure racial and ethnic disparities in the risk of tooth decay. Elastic net regularization was used to select variables to be included in the model among medical conditions, dental procedure types, and individual- and community-level socioeconomic factors. Data were analyzed from January 9 to April 28, 2023. EXPOSURES: Race and ethnicity of children. MAIN OUTCOMES AND MEASURES: The main outcome was diagnosis of tooth decay in either deciduous or permanent teeth, defined as at least 1 decayed, filled, or missing tooth due to caries. An Anderson-Gill model, a time-to-event model for recurrent tooth decay events with time-varying covariates, stratified by age groups (0-5, 6-10, and 11-18 years) was estimated. A nonlinear multiple additive regression tree-based mediation analysis quantified the relative contributions of factors underlying the observed racial and ethnic disparities. RESULTS: Among 61 083 children and adolescents aged 0 to 18 years at baseline (mean [SD] age, 9.9 [4.6] years; 30 773 [50.4%] female), 2654 Black individuals (4.3%), 11 213 Hispanic individuals (18.4%), 42 815 White individuals (70.1%), and 4401 individuals who identified as another race (eg, American Indian, Asian, and Hawaiian and Pacific Islander) (7.2%) were identified. Larger racial and ethnic disparities were observed among children aged 0 to 5 years compared with other age groups (Hispanic children: adjusted hazard ratio [aHR], 1.47; 95% CI, 1.40-1.54; Black children: aHR, 1.30; 95% CI, 1.19-1.42; other race children: aHR, 1.39; 95% CI, 1.29-1.49), compared with White children. For children aged 6 to 10 years, higher risk of tooth decay was observed for Black children (aHR, 1.09; 95% CI, 1.01-1.19) and Hispanic children (aHR, 1.12; 95% CI, 1.07-1.18) compared with White children. For adolescents aged 11 to 18 years, a higher risk of tooth decay was observed only in Black adolescents (aHR, 1.17; 95% CI, 1.06-1.30). A mediation analysis revealed that the association of race and ethnicity with time to first tooth decay became negligible, except for Hispanic and children of other race aged 0 to 5 years, suggesting that mediators explained most of the observed disparities. Insurance type explained the largest proportion of the disparity, ranging from 23.4% (95% CI, 19.8%-30.2%) to 78.9% (95% CI, 59.0%-114.1%), followed by dental procedures (receipt of topical fluoride and restorative procedures) and community-level factors (education attainment and Area Deprivation Index). CONCLUSIONS: In this retrospective cohort study, large proportions of disparities in time to first tooth decay associated with race and ethnicity were explained by insurance type and dental procedure types among children and adolescents. These findings can be applied to develop targeted strategies to reduce oral health disparities.
Jason Poulos, Sharon-Lise T Normand, Katya Zelevinsky, John W Newcomer, Denis Agniel, Haley K Abing, and Marcela Horvitz-Lennon. 2023. “Antipsychotics and the risk of diabetes and death among adults with serious mental illnesses.” Psychol Med, 53, 16, Pp. 7677-7684.Abstract
BACKGROUND: Individuals with schizophrenia exposed to second-generation antipsychotics (SGA) have an increased risk for diabetes, with aripiprazole purportedly a safer drug. Less is known about the drugs' mortality risk or whether serious mental illness (SMI) diagnosis or race/ethnicity modify these effects. METHODS: Authors created a retrospective cohort of non-elderly adults with SMI initiating monotherapy with an SGA (olanzapine, quetiapine, risperidone, and ziprasidone, aripiprazole) or haloperidol during 2008-2013. Three-year diabetes incidence or all-cause death risk differences were estimated between each drug and aripiprazole, the comparator, as well as effects within SMI diagnosis and race/ethnicity. Sensitivity analyses evaluated potential confounding by indication. RESULTS: 38 762 adults, 65% White and 55% with schizophrenia, initiated monotherapy, with haloperidol least (6%) and quetiapine most (26·5%) frequent. Three-year mortality was 5% and diabetes incidence 9.3%. Compared with aripiprazole, haloperidol and olanzapine reduced diabetes risk by 1.9 (95% CI 1.2-2.6) percentage points, or a 18.6 percentage point reduction relative to aripiprazole users' unadjusted risk (10.2%), with risperidone having a smaller advantage. Relative to aripiprazole users' unadjusted risk (3.4%), all antipsychotics increased mortality risk by 1.1-2.2 percentage points, representing 32.4-64.7 percentage point increases. Findings within diagnosis and race/ethnicity were generally consistent with overall findings. Only quetiapine's higher mortality risk held in sensitivity analyses. CONCLUSIONS: Haloperidol's, olanzapine's, and risperidone's lower diabetes risks relative to aripiprazole were not robust in sensitivity analyses but quetiapine's higher mortality risk proved robust. Findings expand the evidence on antipsychotics' risks, suggesting a need for caution in the use of quetiapine among individuals with SMI.
Paula A Rochon, Peter C Austin, Sharon-Lise Normand, Rachel D Savage, Stephanie H Read, Lisa M McCarthy, Vasily Giannakeas, Wei Wu, Rachel Strauss, Xuesong Wang, Simon Chen, and Jerry H Gurwitz. 2023. “Association of a calcium channel blocker and diuretic prescribing cascade with adverse events: A population-based cohort study.” J Am Geriatr Soc.Abstract
BACKGROUND: Prescribing cascades occur when a drug adverse event is misinterpreted as a new medical condition and a second, potentially unnecessary drug, is prescribed to treat the adverse event. The population-level consequences of prescribing cascades remain unknown. METHODS: This population-based cohort study used linked health administrative databases in Ontario, Canada. The study included community-dwelling adults, 66 years of age or older with hypertension and no history of heart failure (HF) or diuretic use in the prior year, newly dispensed a calcium channel blocker (CCB). Individuals subsequently dispensed a diuretic within 90 days of incident CCB dispensing were classified as the prescribing cascade group, and compared to those not dispensed a diuretic, classified as the non-prescribing cascade group. Those with and without a prescribing cascade were matched one-to-one on the propensity score and sex. The primary outcome was a serious adverse event (SAE), which was the composite of emergency room visits and hospitalizations in the 90-day follow-up period. We estimated hazard ratios (HRs) with 95% confidence intervals (CI) for SAE using an Andersen-Gill recurrent events regression model. RESULTS: Among 39,347 older adults with hypertension and no history of HF who were newly dispensed a CCB, 1881 (4.8%) had a new diuretic dispensed within 90 days after CCB initiation. Compared to the non-prescribing cascade group, those in the prescribing cascade group had higher rates of SAEs (HR: 1.21, 95% CI: 1.02-1.43). CONCLUSIONS: The CCB-diuretic prescribing cascade was associated with an increased rate of SAEs, suggesting harm beyond prescribing a second drug therapy. Our study raises awareness of the downstream impact of the CCB-diuretic prescribing cascade at a population level and provides an opportunity for clinicians who identify this prescribing cascade to review their patients' medications to determine if they can be optimized.
Corey C Hardin, Susan Halabi, Daniel Muller, Natalie Koscal, Tim Vining, Sharon-Lise Normand, and Chana A Sacks. 2023. “Bayesian Way.” NEJM Evid, 2, 5, Pp. EVIDstat2300090.Abstract
Bayesian WayThis animated video explores two possible approaches to analyzing data in a randomized controlled trial: "Frequentist" versus "Bayesian."
Laura Elisabeth Gressler, Erika Avila-Tang, Jialin Mao, Alejandra Avalos-Pacheco, Fadia T Shaya, Yelizaveta Torosyan, Alexander Liebeskind, Madris Kinard, Christina D Mack, Sharon-Lise Normand, Mary E Ritchey, and Danica Marinac-Dabic. 2023. “Data sources and applied methods for paclitaxel safety signal discernment.” Front Cardiovasc Med, 10, Pp. 1331142.Abstract
BACKGROUND: Following the identification of a late mortality signal, the Food and Drug Administration (FDA) convened an advisory panel that concluded that additional clinical study data are needed to comprehensively evaluate the late mortality signal observed with the use of drug-coated balloons (DCB) and drug-eluting stent (DES). The objective of this review is to (1) identify and summarize the existing clinical and cohort studies assessing paclitaxel-coated DCBs and DESs, (2) describe and determine the quality of the available data sources for the evaluation of these devices, and (3) present methodologies that can be leveraged for proper signal discernment within available data sources. METHODS: Studies and data sources were identified through comprehensive searches. original research studies, clinical trials, comparative studies, multicenter studies, and observational cohort studies written in the English language and published from January 2007 to November 2021, with a follow-up longer than 36 months, were included in the review. Data quality of available data sources identified was assessed in three groupings. Moreover, accepted data-driven methodologies that may help circumvent the limitations of the extracted studies and data sources were extracted and described. RESULTS: There were 39 studies and data sources identified. This included 19 randomized clinical trials, nine single-arm studies, eight registries, three administrative claims, and electronic health records. Methodologies focusing on the use of existing premarket clinical data, the incorporation of all contributed patient time, the use of aggregated data, approaches for individual-level data, machine learning and artificial intelligence approaches, Bayesian approaches, and the combination of various datasets were summarized. CONCLUSION: Despite the multitude of available studies over the course of eleven years following the first clinical trial, the FDA-convened advisory panel found them insufficient for comprehensively assessing the late-mortality signal. High-quality data sources with the capabilities of employing advanced statistical methodologies are needed to detect potential safety signals in a timely manner and allow regulatory bodies to act quickly when a safety signal is detected.
Marcela Horvitz-Lennon, Emily Leckman-Westin, Molly Finnerty, Junghye Jeong, Jeannette Tsuei, Katya Zelevinsky, Qingxian Chen, and Sharon-Lise T Normand. 2023. “Healthcare Access for a Diverse Population with Schizophrenia Following the Onset of the COVID-19 Pandemic.” Community Ment Health J, Pp. 1-9.Abstract
COVID-19 has had a disproportionate impact on the most disadvantaged members of society, including minorities and those with disabling chronic illnesses such as schizophrenia. We examined the pandemic's impacts among New York State's Medicaid beneficiaries with schizophrenia in the immediate post-pandemic surge period, with a focus on equity of access to critical healthcare. We compared changes in utilization of key behavioral health outpatient services and inpatient services for life-threatening conditions between the pre-pandemic and surge periods for White and non-White beneficiaries. We found racial and ethnic differences across all outcomes, with most differences stable over time. The exception was pneumonia admissions-while no differences existed in the pre-pandemic period, Black and Latinx beneficiaries were less likely than Whites to be hospitalized in the surge period despite minorities' heavier COVID-19 disease burden. The emergence of racial and ethnic differences in access to scarce life-preserving healthcare may hold lessons for future crises.
Corey C Hardin, Daniel Muller, Suellen Li, Michael Fralick, Tim Vining, Sharon-Lise Normand, and Chana A Sacks. 2023. “How Censoring Works.” NEJM Evid, 2, 10, Pp. EVIDstat2300205.Abstract
How Censoring WorksA common challenge in clinical research is determining the time to occurrence of a given event. This animated video explores the concept of censoring in survival analysis and how investigators deal with ambiguity in the time of an event's occurrence.
Suellen Li, Corey C Hardin, Daniel Muller, Emily Ling, Tim Vining, Sharon-Lise Normand, and Chana A Sacks. 2023. “How Statistical Power Works.” NEJM Evid, 2, 12, Pp. EVIDstat2300283.Abstract
How Statistical Power WorksThis Stats, STAT! animated video explores the concept of statistical power and explains how clinical investigators determine how many participants to enroll in a randomized trial.
Nicole M Benson, Zhiyou Yang, Vicki Fung, Sharon-Lise Normand, Matcheri S Keshavan, Dost Öngür, and John Hsu. 2023. “Medical and Psychiatric Care Preceding the First Psychotic Disorder Diagnosis.” Schizophr Bull.Abstract
BACKGROUND: Individuals with psychotic symptoms experience substantial morbidity and have shortened life expectancies; early treatment may mitigate the worst effects. Understanding care preceding a first psychotic disorder diagnosis is critical to inform early detection and intervention. STUDY DESIGN: In this observational cohort study using comprehensive information from the Massachusetts All-Payer Claims Database, we identified the first psychotic disorder diagnosis in 2016, excluding those with historical psychotic disorder diagnoses in the prior 48 months among those continuous enrollment data. We reviewed visits, medications, and hospitalizations 2012-2016. We used logistic regression to examine characteristics associated with pre-diagnosis antipsychotic use. STUDY RESULTS: There were 2505 individuals aged 15-35 years (146 per 100 000 similarly aged individuals in the database) with a new psychotic disorder diagnosis in 2016. Most (97%) had at least one outpatient visit in the preceding 48 months; 89% had a prior mental health diagnosis unrelated to psychosis (eg, anxiety [60%], depression [60%]). Many received psychotropic medications (77%), including antipsychotic medications (46%), and 68% had a visit for injury or trauma during the preceding 48 months. Characteristics associated with filling an antipsychotic medication before the psychotic disorder diagnosis included male sex and Medicaid insurance at psychosis diagnosis. CONCLUSIONS: In this insured population of Massachusetts residents with a new psychotic disorder diagnosis, nearly all had some healthcare utilization, visits for injury or trauma were common, and nearly half filled an antipsychotic medication in the preceding 48 months. These patterns of care could represent either pre-disease signals, delays, or both in receiving a formal diagnosis.
Nicole M Benson, Zhiyou Yang, Vicki Fung, Sharon-Lise Normand, Matcheri S Keshavan, Dost Öngür, and John Hsu. 2023. “Medical and Psychiatric Care Preceding the First Psychotic Disorder Diagnosis.” Schizophr Bull.Abstract
BACKGROUND: Individuals with psychotic symptoms experience substantial morbidity and have shortened life expectancies; early treatment may mitigate the worst effects. Understanding care preceding a first psychotic disorder diagnosis is critical to inform early detection and intervention. STUDY DESIGN: In this observational cohort study using comprehensive information from the Massachusetts All-Payer Claims Database, we identified the first psychotic disorder diagnosis in 2016, excluding those with historical psychotic disorder diagnoses in the prior 48 months among those continuous enrollment data. We reviewed visits, medications, and hospitalizations 2012-2016. We used logistic regression to examine characteristics associated with pre-diagnosis antipsychotic use. STUDY RESULTS: There were 2505 individuals aged 15-35 years (146 per 100 000 similarly aged individuals in the database) with a new psychotic disorder diagnosis in 2016. Most (97%) had at least one outpatient visit in the preceding 48 months; 89% had a prior mental health diagnosis unrelated to psychosis (eg, anxiety [60%], depression [60%]). Many received psychotropic medications (77%), including antipsychotic medications (46%), and 68% had a visit for injury or trauma during the preceding 48 months. Characteristics associated with filling an antipsychotic medication before the psychotic disorder diagnosis included male sex and Medicaid insurance at psychosis diagnosis. CONCLUSIONS: In this insured population of Massachusetts residents with a new psychotic disorder diagnosis, nearly all had some healthcare utilization, visits for injury or trauma were common, and nearly half filled an antipsychotic medication in the preceding 48 months. These patterns of care could represent either pre-disease signals, delays, or both in receiving a formal diagnosis.
Natalia Festa, Nina Katz-Christy, Max Weiss, Rebecca Lisk, Sharon-Lise Normand, David C Grabowski, Joseph P Newhouse, and John Hsu. 2023. “Nursing home infection control strategies during the COVID-19 pandemic.” J Am Geriatr Soc, 71, 8, Pp. 2593-2600.Abstract
BACKGROUND: The American Rescue Plan Act of 2021 awarded $500 million toward scaling "strike teams" to mitigate the impact of Coronavirus Disease 2019 (COVID-19) within nursing homes. The Massachusetts Nursing Facility Accountability and Support Package (NFASP) piloted one such model during the first weeks of the pandemic, providing nursing homes financial, administrative, and educational support. For a subset of nursing homes deemed high-risk, the state offered supplemental, in-person technical infection control support. METHODS: Using state death certificate data and federal nursing home occupancy data, we examined longitudinal all-cause mortality per 100,000 residents and changes in occupancy across NFASP participants and subgroups that varied in their receipt of the supplemental intervention. RESULTS: Nursing home mortality peaked in the weeks preceding the NFASP, with a steeper increase among those receiving the supplemental intervention. There were contemporaneous declines in weekly occupancy. The potential for temporal confounding and differential selection across NFASP subgroups precluded estimation of causal effects of the intervention on mortality. CONCLUSIONS: We offer policy and design suggestions for future strike team iterations that could inform the allocation of state and federal funding. We recommend expanded data collection infrastructure and, ideally, randomized assignment to intervention subgroups to support causal inference as strike team models are scaled under the direction of state and federal agencies.
Yun Wang, Noel Eldridge, Mark L Metersky, David Rodrick, Sheila Eckenrode, Jasie Mathew, Deron H Galusha, Andrea A Peterson, David Hunt, Sharon-Lise T Normand, and Harlan M Krumholz. 2023. “Relationship Between In-Hospital Adverse Events and Hospital Performance on 30-Day All-cause Mortality and Readmission for Patients With Heart Failure.” Circ Cardiovasc Qual Outcomes, 16, 7, Pp. e009573.Abstract
BACKGROUND: Hospitals with high mortality and readmission rates for patients with heart failure (HF) might also perform poorly in other quality concepts. We sought to evaluate the association between hospital performance on mortality and readmission with hospital performance rates of safety adverse events. METHODS: This cross-sectional study linked the 2009 to 2019 patient-level adverse events data from the Medicare Patient Safety Monitoring System, a randomly selected medical records-abstracted patient safety database, to the 2005 to 2016 hospital-level HF-specific 30-day all-cause mortality and readmissions data from the United States Centers for Medicare & Medicaid Services. Hospitals were classified to one of 3 performance categories based on their risk-standardized 30-day all-cause mortality and readmission rates: better (both in <25th percentile), worse (both >75th percentile), and average (otherwise). Our main outcome was the occurrence (yes/no) of one or more adverse events during hospitalization. A mixed-effect model was fit to assess the relationship between a patient's risk of having adverse events and hospital performance categories, adjusted for patient and hospital characteristics. RESULTS: The study included 39 597 patients with HF from 3108 hospitals, of which 252 hospitals (8.1%) and 215 (6.9%) were in the better and worse categories, respectively. The rate of patients with one or more adverse events during a hospitalization was 12.5% (95% CI, 12.1-12.8). Compared with patients admitted to better hospitals, patients admitted to worse hospitals had a higher risk of one or more hospital-acquired adverse events (adjusted risk ratio, 1.24 [95% CI, 1.06-1.44]). CONCLUSIONS: Patients admitted with HF to hospitals with high 30-day all-cause mortality and readmission rates had a higher risk of in-hospital adverse events. There may be common quality issues among these 3 measure concepts in these hospitals that produce poor performance for patients with HF.
Andrew D Wilcock, Haiden A Huskamp, Alisa B Busch, Sharon-Lise T Normand, Lori Uscher-Pines, Pushpa V Raja, Jose R Zubizarreta, Michael L Barnett, and Ateev Mehrotra. 2023. “Use of Telemedicine and Quality of Care Among Medicare Enrollees With Serious Mental Illness.” JAMA Health Forum, 4, 10, Pp. e233648.Abstract
IMPORTANCE: During the COVID-19 pandemic, a large fraction of mental health care was provided via telemedicine. The implications of this shift in care for use of mental health service and quality of care have not been characterized. OBJECTIVE: To compare changes in care patterns and quality during the first year of the pandemic among Medicare beneficiaries with serious mental illness (schizophrenia or bipolar I disorder) cared for at practices with higher vs lower telemedicine use. DESIGN, SETTING, AND PARTICIPANTS: In this cohort study, Medicare fee-for-service beneficiaries with schizophrenia or bipolar I disorder were attributed to specialty mental health practices that delivered the majority of their mental health care in 2019. Practices were categorized into 3 groups based on the proportion of telemental health visits provided during the first year of the pandemic (March 2020-February 2021): lowest use (0%-49%), middle use (50%-89%), or highest use (90%-100%). Across the 3 groups of practices, differential changes in patient outcomes were calculated from the year before the pandemic started to the year after. These changes were also compared with differential changes from a 2-year prepandemic period. Analyses were conducted in November 2022. EXPOSURE: Practice-level use of telemedicine during the first year of the COVID-19 pandemic. MAIN OUTCOMES AND MEASURES: The primary outcome was the total number of mental health visits (telemedicine plus in-person) per person. Secondary outcomes included the number of acute hospital and emergency department encounters, all-cause mortality, and quality outcomes, including adherence to antipsychotic and mood-stabilizing medications (as measured by the number of months of medication fills) and 7- and 30-day outpatient follow-up rates after discharge for a mental health hospitalization. RESULTS: The pandemic cohort included 120 050 Medicare beneficiaries (mean [SD] age, 56.5 [14.5] years; 66 638 females [55.5%]) with serious mental illness. Compared with prepandemic changes and relative to patients receiving care at practices with the lowest telemedicine use: patients receiving care at practices in the middle and highest telemedicine use groups had 1.11 (95% CI, 0.45-1.76) and 1.94 (95% CI, 1.28-2.59) more mental health visits per patient per year (or 7.5% [95% CI, 3.0%-11.9%] and 13.0% [95% CI, 8.6%-17.4%] more mental health visits per year, respectively). Among patients of practices with middle and highest telemedicine use, changes in adherence to antipsychotic and mood-stabilizing medications were -0.4% (95% CI, -1.3% to 0.5%) and -0.1% (95% CI, -1.0% to 0.8%), and hospital and emergency department use for any reason changed by 2.4% (95% CI, -1.5% to 6.2%) and 2.8% (95% CI, -1.2% to 6.8%), respectively. There were no significant differential changes in postdischarge follow-up or mortality rates according to the level of telemedicine use. CONCLUSIONS AND RELEVANCE: In this cohort study of Medicare beneficiaries with serious mental illness, patients receiving care from practices that had a higher level of telemedicine use during the COVID-19 pandemic had more mental health visits per year compared with prepandemic levels, with no differential changes in other observed quality metrics over the same period.
2022
Yun Wang, Noel Eldridge, Mark L Metersky, David Rodrick, Constance Faniel, Sheila Eckenrode, Jasie Mathew, Deron H Galusha, Anila Tasimi, Shih-Yieh Ho, Lisa Jaser, Andrea Peterson, Sharon-Lise T Normand, and Harlan M Krumholz. 2022. “Analysis of Hospital-Level Readmission Rates and Variation in Adverse Events Among Patients With Pneumonia in the United States.” JAMA Netw Open, 5, 5, Pp. e2214586.Abstract
IMPORTANCE: It is known that hospitalized patients who experience adverse events are at greater risk of readmission; however, it is unknown whether patients admitted to hospitals with higher risk-standardized readmission rates had a higher risk of in-hospital adverse events. OBJECTIVE: To evaluate whether patients with pneumonia admitted to hospitals with higher risk-standardized readmission rates had a higher risk of adverse events. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study linked patient-level adverse events data from the Medicare Patient Safety Monitoring System (MPSMS), a randomly selected medical record abstracted database, to the hospital-level pneumonia-specific all-cause readmissions data from the Centers for Medicare & Medicaid Services. Patients with pneumonia discharged from July 1, 2010, through December 31, 2019, in the MPSMS data were included. Hospital performance on readmissions was determined by the risk-standardized 30-day all-cause readmission rate. Mixed-effects models were used to examine the association between adverse events and hospital performance on readmissions, adjusted for patient and hospital characteristics. Analysis was completed from October 2019 through July 2020 for data from 2010 to 2017 and from March through April 2022 for data from 2018 to 2019. EXPOSURES: Patients hospitalized for pneumonia. MAIN OUTCOMES AND MEASURES: Adverse events were measured by the rate of occurrence of hospital-acquired events and the number of events per 1000 discharges. RESULTS: The sample included 46 047 patients with pneumonia, with a median (IQR) age of 71 (58-82) years, with 23 943 (52.0%) women, 5305 (11.5%) Black individuals, 37 763 (82.0%) White individuals, and 2979 (6.5%) individuals identifying as another race, across 2590 hospitals. The median hospital-specific risk-standardized readmission rate was 17.0% (95% CI, 16.3%-17.7%), the occurrence rate of adverse events was 2.6% (95% CI, 2.54%-2.65%), and the number of adverse events per 1000 discharges was 157.3 (95% CI, 152.3-162.5). An increase by 1 IQR in the readmission rate was associated with a relative 13% higher patient risk of adverse events (adjusted odds ratio, 1.13; 95% CI, 1.08-1.17) and 5.0 (95% CI, 2.8-7.2) more adverse events per 1000 discharges at the patient and hospital levels, respectively. CONCLUSIONS AND RELEVANCE: Patients with pneumonia admitted to hospitals with high all-cause readmission rates were more likely to develop adverse events during the index hospitalization. This finding strengthens the evidence that readmission rates reflect the quality of hospital care for pneumonia.
Bianca Maria Maglia Orlandi, Omar Asdrúbal Vilca Mejia, Jennifer Loría Sorio, Pedro de Barros E Silva, Marco Antonio Praça Oliveira, Marcelo Arruda Nakazone, Marcos Gradim Tiveron, Valquíria Pelliser Campagnucci, Luiz Augusto Ferreira Lisboa, Jorge Zubelli, Sharon-Lise Normand, and Fabio Biscegli Jatene. 2022. “Author Correction: Performance of a novel risk model for deep sternal wound infection after coronary artery bypass grafting.” Sci Rep, 12, 1, Pp. 19850.

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