Association Between the Modified Frailty Index and Outcomes Following Lobectomy.

INTRODUCTION: Elective thoracic surgery is safe in well-selected elderly patients. The association of frailty with postoperative morbidity in elective-lobectomy patients is understudied. We examined frailty as defined by abbreviated modified frailty index (mFI-5), mFI-11 in the thoracic surgery population, and the correlation between frailty and postoperative complications. METHODS: We studied outcomes of patients in two cohorts, 2010-2012 and 2013-2019, from the National Surgical Quality Improvement Program (NSQIP) database and used multivariable logistic regression models to predict all postoperative morbidity, mortality, and major morbidity. The mFI-5 could be calculated for all subjects (both 2010-2012, and 2013-2019); the mFI-11 could only be calculated for the 2010-2012 cohort. Patient frailty was defined as mFI≥3 (with either index). We used odds ratios (ORs) to examine associations of preoperative characteristics with postoperative complications and C-statistics to assess overall predictive power. RESULTS: Complications were less prevalent in the 2013-2019 cohort (17.9% versus 19.5%, P = 0.008). Open lobectomies were more common in the 2010-2012 cohort (53.9% versus 34.6%) and were strongly associated with postoperative morbidity and mortality (ORs >1.5) in both cohorts. Each frailty measure was associated with morbidity and mortality (ORs >1.4) after adjusting for other significant preoperative factors. Models on the 2010-2012 cohort had nearly identical C-statistics using the mFI-11 versus mFI-5 frailty indices (0.6142 versus 0.6139; P > 0.8). CONCLUSIONS: Frailty, as captured in the mFI-5, is a significant associated factor of postoperative morbidity and mortality following elective lobectomies. As a modifiable risk factor, frailty should be considered in surgical decision-making and when counseling patients regarding perioperative risks.

Predicting Adolescent Intervention Non-responsiveness for Precision HIV Prevention Using Machine Learning.

Interventions to teach protective behaviors may be differentially effective within an adolescent population. Identifying the characteristics of youth who are less likely to respond to an intervention can guide program modifications to improve its effectiveness. Using comprehensive longitudinal data on adolescent risk behaviors, perceptions, sensation-seeking, peer and family influence, and neighborhood risk factors from 2564 grade 10-12 students in The Bahamas, this study employs machine learning approaches (support vector machines, logistic regression, decision tree, and random forest) to identify important predictors of non-responsiveness for precision prevention. We used 80% of the data to train the models and the rest for model testing. Among different machine learning algorithms, the random forest model using longitudinal data and the Boruta feature selection approach predicted intervention non-responsiveness best, achieving sensitivity of 85.4%, specificity of 78.4% and AUROC of 0.93 on the training data, and sensitivity of 84.3%, specificity of 67.1%, and AUROC of 0.85 on the test data. Key predictors include self-efficacy, perceived response cost, parent monitoring, vulnerability, response efficacy, HIV/AIDS knowledge, communication about condom use, and severity of HIV/STI. Machine learning can yield powerful predictive models to identify adolescents who are unlikely to respond to an intervention. Such models can guide the development of alternative strategies that may be more effective with intervention non-responders.

Maintaining Program Fidelity in a Changing World: National Implementation of a School-Based HIV Prevention Program.

Large-scale, evidence-based interventions face challenges to program fidelity of implementation. We developed implementation strategies to support teachers implementing an evidence-based HIV prevention program in schools, Focus on Youth in The Caribbean (FOYC) and Caribbean Informed Parents and Children Together (CImPACT) in The Bahamas. We examined the effects of these implementation strategies on teachers' implementation in the subsequent year after the initial implementation during the COVID-19 pandemic. Data were collected from 79 Grade 6 teachers in 24 government elementary schools. Teachers completed training workshops and a pre-implementation questionnaire to record their characteristics and perceptions that might affect their program fidelity. School coordinators and peer mentors provided teachers with monitoring, feedback, and mentoring. In Year 1, teachers on average taught 79.3% of the sessions and 80.8% of core activities; teachers in Year 2 covered 84.2% of sessions and 72.9% of the core activities. Teachers with "good" or "excellent" school coordinators in the second year taught significantly more sessions on average (7.8 vs. 7.0, t = 2.04, P < 0.05) and more core activities (26.3 vs. 23.0, t = 2.41, P < 0.05) than teachers with "satisfactory" coordinators. Teachers who had a "good" or "satisfactory" mentor taught more sessions than teachers who did not have a mentor (7.9 vs. 7.3; t = 2.22; P = 0.03). Two-level mixed-effects model analysis indicated that teachers' program fidelity in Year 1, confidence in the execution of core activities, and school coordinators' performance were significantly associated with Year 2 implementation dose. Implementation of FOYC + CImPACT was significantly associated with improved student outcomes. Teachers maintained high fidelity to a comprehensive HIV prevention program over 2 years during the COVID-19 pandemic. Future program implementers should consider additional implementation support to improve the implementation of school-based programs.

Association of Social Determinants of Health With Adherence to Second-generation Antipsychotics for People With Bipolar Disorders in a Medicaid Population.

BACKGROUND: About 7 million people, 2.8% of US adults, have bipolar disorder (BD). While second-generation antipsychotics (SGA) are indicated as acute and maintenance treatments for BD, therapeutic success requires medication adherence and reported nonadherence estimates to range as high as 60%. Identifying patient risk factors for nonadherence is important for reducing it. OBJECTIVE: The objective of this study was to quantify the associations of risk factors, including social determinants of health, with SGA nonadherence among patients with BD. METHODS: In this cross-sectional study of 2015-2017 MassHealth Medicaid data, we examined several definitions of adherence and used logistic regression to identify risk factors for nonadherence (medication possession ratio <0.8) among all adults aged 18-64 diagnosed with BD who could be followed for 12 months following SGA initiation. RESULTS: Among 5197 patients, the mean (±SD) age was 37.7 (±11.4) years, and 42.3% were men. Almost half (47.7%) of patients were nonadherent to SGAs when measured by medication possession ratio. The prevalence of nonadherence peaked at middle age for men and younger for women. Nonadherence was less common among Massachusetts' Department of Mental Health clients (odds ratio=0.60, 95% confidence limit: 0.48-0.74) and among those who used other psychotropic medications (odds ratios between 0.45 and 0.81); in contrast, increase in neighborhood socioeconomic stress was associated with increased odds of nonadherence. CONCLUSIONS/IMPLICATIONS: Adherence to SGA treatment is suboptimal among people with BD. Recognizing risk factors, including those related to social determinants of health, can help target interventions to improve adherence for people at high risk and has implications for adherence-based quality measures.

Complex Patients Have More Emergency Visits: Don't Punish the Systems That Serve Them.

IMPORTANCE: Better patient management can reduce emergency department (ED) use. Performance measures should reward plans for reducing utilization by predictably high-use patients, rather than rewarding plans that shun them. OBJECTIVE: The objective of this study was to develop a quality measure for ED use for people diagnosed with serious mental illness or substance use disorder, accounting for both medical and social determinants of health (SDH) risks. DESIGN: Regression modeling to predict ED use rates using diagnosis-based and SDH-augmented models, to compare accuracy overall and for vulnerable populations. SETTING: MassHealth, Massachusetts' Medicaid and Children's Health Insurance Program. PARTICIPANTS: MassHealth members ages 18-64, continuously enrolled for the calendar year 2016, with a diagnosis of serious mental illness or substance use disorder. EXPOSURES: Diagnosis-based model predictors are diagnoses from medical encounters, age, and sex. Additional SDH predictors describe housing problems, behavioral health issues, disability, and neighborhood-level stress. MAIN OUTCOME AND MEASURES: We predicted ED use rates: (1) using age/sex and distinguishing between single or dual diagnoses; (2) adding summarized medical risk (DxCG); and (3) further adding social risk (SDH). RESULTS: Among 144,981 study subjects, 57% were women, 25% dually diagnosed, 67% White/non-Hispanic, 18% unstably housed, and 37% disabled. Utilization was higher by 77% for those dually diagnosed, 50% for members with housing problems, and 18% for members living in the highest-stress neighborhoods. SDH modeling predicted best for these high-use populations and was most accurate for plans with complex patients. CONCLUSION: To set appropriate benchmarks for comparing health plans, quality measures for ED visits should be adjusted for both medical and social risks.

The association between ambient air pollution and birth defects in five major ethnic groups in Liuzhou, China.

BACKGROUND: Studies suggest that exposure to ambient air pollution during pregnancy may be associated with increased risks of birth defects (BDs), but conclusions have been inconsistent. This study describes the ethnic distribution of major BDs and examines the relationship between air pollution and BDs among different ethnic groups in Liuzhou city, China. METHODS: Surveillance data of infants born in 114 registered hospitals in Liuzhou in 2019 were analyzed to determine the epidemiology of BDs across five major ethnic groups. Concentrations of six air pollutants (PM2.5, PM10, SO2, CO, NO2, O3) were obtained from the Liuzhou Environmental Protection Bureau. Logistic regression was used to examine the associations between ambient air pollution exposure and risk of BDs. RESULTS: Among 32,549 infants, 635 infants had BDs, yielding a prevalence of 19.5 per 1000 perinatal infants. Dong ethnic group had the highest prevalence of BDs (2.59%), followed by Yao (2.57%), Miao (2.35%), Zhuang (2.07%), and Han (1.75%). Relative to the Han ethnic group, infants from Zhuang, Miao, Yao and Dong groups had lower risks of congenital heart disease, polydactyly, and hypospadias. The Zhuang ethnic group had higher risks of severe thalassemia, cleft lip and/or palate, and syndactyls. Overall BDs were positively correlated with air pollutants PM10 (aOR =1.14, 95% CI:1.12 ~ 2.43; aOR =1.51, 95% CI:1.13 ~ 2.03 for per 10µg/mg3 increment) and CO (aOR =1.36, 95% CI:1.14 ~ 2.48; aOR =1.75, 95% CI:1.02 ~ 3.61 for every 1 mg /m3 increment) in second and third month of pregnancy. SO2 was also significantly associated with BDs in the second month before the pregnancy (aOR = 1.31; 95% CI: 1.20 ~ 3.22) and third month of pregnancy (aOR =1.75; 95% CI:1.02 ~ 3.61). Congenital heart disease, polydactyl, cleft lip and/or palate were also significantly associated with PM10, SO2 and CO exposures. However, no significant association was found between birth defects and O3, PM2.5 and NO2 exposures (P > 0.05). CONCLUSION: This study provides a comprehensive description of ethnic differences in BDs in Southwest China and broadens the evidence of the association between air pollution exposure during gestation and BDs.

Secure Messaging, Diabetes Self-management, and the Importance of Patient Autonomy: a Mixed Methods Study.

BACKGROUND: Diabetes is a complex, chronic disease that requires patients' effective self-management between clinical visits; this in turn relies on patient self-efficacy. The support of patient autonomy from healthcare providers is associated with better self-management and greater diabetes self-efficacy. Effective provider-patient secure messaging (SM) through patient portals may improve disease self-management and self-efficacy. SM that supports patients' sense of autonomy may mediate this effect by providing patients ready access to their health information and better communication with their clinical teams. OBJECTIVE: We examined the association between healthcare team-initiated SM and diabetes self-management and self-efficacy, and whether this association was mediated by patients' perceptions of autonomy support from their healthcare teams. DESIGN: We surveyed and analyzed content of messages sent to a sample of patients living with diabetes who use the SM feature on the VA's My HealtheVet patient portal. PARTICIPANTS: Four hundred forty-six veterans with type 2 diabetes who were sustained users of SM. MAIN MEASURES: Proactive (healthcare team-initiated) SM (0 or ≥ 1 messages); perceived autonomy support; diabetes self-management; diabetes self-efficacy. KEY RESULTS: Patients who received at least one proactive SM from their clinical team were significantly more likely to engage in better diabetes self-management and report a higher sense of diabetes self-efficacy. This relationship was mediated by the patient's perception of autonomy support. The majority of proactive SM discussed scheduling, referrals, or other administrative content. Patients' responses to team-initiated communication promoted patient engagement in diabetes self-management behaviors. CONCLUSIONS: Perceived autonomy support is important for diabetes self-management and self-efficacy. Proactive communication from clinical teams to patients can help to foster a patient's sense of autonomy and encourage better diabetes self-management and self-efficacy.

Identifying monoclonal gammopathy of undetermined significance in electronic health data.

PURPOSE: Monoclonal gammopathy of undetermined significance (MGUS) is a prevalent yet largely asymptomatic precursor to multiple myeloma. Patients with MGUS must undergo regular surveillance and testing, with few known predictors of progression. We developed an algorithm to identify MGUS patients in electronic health data to facilitate large-scale, population-based studies of this premalignant condition. METHODS: We developed a four-step algorithm using electronic health record and health claims data from men and women aged 50 years or older receiving care from a large, multispecialty medical group between 2007 and 2015. The case definition required patients to have at least two MGUS ICD-9 diagnosis codes within 12 months, at least one serum and/or urine protein electrophoresis and one immunofixation test, and at least one in-office hematology/oncology visit. Medical charts for selected cases were abstracted then adjudicated independently by two physicians. We assessed algorithm validity by positive predictive value (PPV). RESULTS: We identified 833 people with at least two MGUS diagnosis codes; 429 (52%) met all four algorithm criteria. We randomly selected 252 charts for review, including 206 from patients meeting all four algorithm criteria. The PPV for the 206 algorithm-identified charts was 76% (95% CI, 70%-82%). Among the 49 cases deemed to be false positives (24%), 33 were judged to have multiple myeloma or another lymphoproliferative condition, such as lymphoma. CONCLUSIONS: We developed a simple algorithm that identified MGUS cases in electronic health data with reasonable accuracy. Inclusion of additional steps to eliminate cases with malignant disease may improve algorithm performance.

Twenty-five year trends (1986-2011) in hospital incidence and case-fatality rates of ventricular tachycardia and ventricular fibrillation complicating acute myocardial infarction.

BACKGROUND: Long-term trends in the incidence rates (IRs) and hospital case-fatality rates (CFRs) of ventricular tachycardia (VT) and ventricular fibrillation (VF) among patients hospitalized with acute myocardial infarction (AMI) have not been recently examined. METHODS: We used data from 11,825 patients hospitalized with AMI at all 11 medical centers in central Massachusetts on a biennial basis between 1986 and 2011. Multivariable adjusted logistic regression modeling was used to examine trends in hospital IRs and CFRs of VT and VF complicating AMI. RESULTS: The median age of the study population was 71 years, 57.9% were men, and 94.7% were white. The hospital IRs declined from 14.3% in 1986/1988 to 10.5% in 2009/2011 for VT and from 8.2% to 1.7% for VF. The in-hospital CFRs declined from 27.7% to 6.9% for VT and from 49.6% to 36.0% for VF between 1986/1988 and 2009/2011, respectively. The IRs of both early (<48 hours) and late VT and VF declined over time, with greater declines in those of late VT and VF. The incidence rates of VT declined similarly for patients with either an ST-segment elevation myocardial infarction (STEMI) or non-STEMI, whereas they only declined in those with VF and a STEMI. CONCLUSIONS: The hospital IRs and CHRs of VT and VF complicating AMI have declined over time, likely because of changes in acute monitoring and treatment practices. Despite these encouraging trends, efforts remain needed to identify patients at risk for these serious ventricular arrhythmias so that preventive and treatment strategies might be implemented as necessary.

Emergency Department and Primary Care Use in Massachusetts 5 Years After Health Reform.

OBJECTIVE: Conceptually, access to primary care (through insurance) should reduce emergency department (ED) visits for primary care sensitive (PCS) conditions. We sought to identify characteristics of insured Massachusetts residents associated with PCS ED use, and compare such use for public versus private insurees. POPULATION AND SETTING: People under age 65 in the Massachusetts All-Payer Claims Data, 2011-2012. STUDY DESIGN: Retrospective, observational analysis of PCS ED use with nonurgent, urgent/primary care treatable, and urgent/potentially avoidable visits being considered PCS. We predicted utilization in 2012 using multivariable regression models and data available in 2011 administrative records. PRINCIPAL FINDINGS: Among 2,269,475 nonelderly Massachusetts residents, 40% had public insurance. Among public insurees, PCS ED use was higher than for private (mean, 36.5 vs. 9.0 per 100 persons; adjusted risk ratio, 2.53; 95% confidence limits, 2.49-2.56), while having any primary care visit was less common (70% vs. 83%), as was having any visit to one's own (attributed) primary care provider (38% vs. 44%). CONCLUSIONS: Public insurance was associated with less access to primary care and more PCS ED use; statewide labor shortages and low reimbursement rates from public insurance may have provided inadequate access to care that might otherwise have helped reduce PCS ED use.