Preferences for Postacute Care at Home vs Facilities.

Importance: Two in 5 US hospital stays result in rehabilitative postacute care, typically through skilled nursing facilities (SNFs) or home health agencies (HHAs). However, a lack of clear guidelines and understanding of patient and caregiver preferences make it challenging to promote high-value patient-centered care. Objective: To assess preferences and willingness to pay for facility-based vs home-based postacute care among patients and caregivers, considering demographic variations. Design, Setting, and Participants: In September 2022, a nationally representative survey was conducted with participants 45 years or older. Using a discrete choice experiment, participants acting as patients or caregivers chose between facility-based and home-based postacute care that best met their preferences, needs, and family conditions. Survey weights were applied to generate nationally representative estimates. Main Outcomes and Measures: Preferences and willingness to pay for various attributes of postacute care settings were assessed, examining variation based on demographic factors, socioeconomic status, job security, and previous care experiences. Results: A total of 2077 adults were invited to participate in the survey; 1555 (74.9%) completed the survey. In the weighted sample, 52.9% of participants were women, 6.5% were Asian or Pacific Islander, 1.7% were American Indian or Alaska Native, 11.2% were Black or African American, 78.4% were White; the mean (SD) age was 62.6 (9.6) years; and there was a survey completion rate of 74.9%. Patients and caregivers showed a substantial willingness to pay for home-based and high-quality care. Patients and caregivers were willing to pay an additional $58.08 per day (95% CI, 45.32-70.83) and $45.54 per day (95% CI, 31.09-59.99) for HHA care compared with a shared SNF room, respectively. However, increased demands on caregiver time within an HHA scenario and socioeconomic challenges, such as insecure employment, shifted caregivers' preferences toward facility-based care. There was a strong aversion to below average quality. To avoid below average SNF care, patients and caregivers were willing to pay $75.21 per day (95% CI, 61.68-88.75) and $79.10 per day (95% CI, 63.29-94.91) compared with average-quality care, respectively. Additionally, prior awareness and experience with postacute care was associated with willingness to pay for home-based care. No differences in preferences among patients and caregivers based on race, educational background, urban or rural residence, general health status, or housing type were observed. Conclusions and Relevance: The findings of this survey study underscore a prevailing preference for home-based postacute care, aligning with current policy trends. However, attention is warranted for disadvantaged groups who are potentially overlooked during the shift toward home-based care, particularly those facing caregiver constraints and socioeconomic hardships. Ensuring equitable support and improved quality measure tools are crucial for promoting patient-centric postacute care, with emphasis on addressing the needs of marginalized groups.

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes.

Weighting is a general and often-used method for statistical adjustment. Weighting has two objectives: first, to balance covariate distributions, and second, to ensure that the weights have minimal dispersion and thus produce a more stable estimator. A recent, increasingly common approach directly optimizes the weights toward these two objectives. However, this approach has not yet been feasible in large-scale datasets when investigators wish to flexibly balance general basis functions in an extended feature space. To address this practical problem, we describe a scalable and flexible approach to weighting that integrates a basis expansion in a reproducing kernel Hilbert space with state-of-the-art convex optimization techniques. Specifically, we use the rank-restricted Nyström method to efficiently compute a kernel basis for balancing in nearly linear time and space, and then use the specialized first-order alternating direction method of multipliers to rapidly find the optimal weights. In an extensive simulation study, we provide new insights into the performance of weighting estimators in large datasets, showing that the proposed approach substantially outperforms others in terms of accuracy and speed. Finally, we use this weighting approach to conduct a national study of the relationship between hospital profit status and heart attack outcomes in a comprehensive dataset of 1.27 million patients. We find that for-profit hospitals use interventional cardiology to treat heart attacks at similar rates as other hospitals but have higher mortality and readmission rates.

Firearm Injuries In Children And Adolescents: Health And Economic Consequences Among Survivors And Family Members.

More US children and adolescents today die from firearms than any other cause, and many more sustain firearm injuries and survive. The clinical and economic impact of these firearm injuries on survivors and family members remains poorly understood. Using 2007-21 commercial health insurance claims data, we studied 2,052 child and adolescent survivors compared to 9,983 matched controls who did not incur firearm injuries, along with 6,209 family members of survivors compared to 29,877 matched controls, and 265 family members of decedents compared to 1,263 matched controls. Through one year after firearm injury, child and adolescent survivors experienced a 117 percent increase in pain disorders, a 68 percent increase in psychiatric disorders, and a 144 percent increase in substance use disorders relative to the controls. Survivors' health care spending increased by an average of $34,884-a 17.1-fold increase-with 95 percent paid by insurers or employers. Parents of survivors experienced a 30-31 percent increase in psychiatric disorders, with 75 percent more mental health visits by mothers, and 5-14 percent reductions in mothers' and siblings' routine medical care. Family members of decedents experienced substantially larger 2.3- to 5.3-fold increases in psychiatric disorders, with at least 15.3-fold more mental health visits among parents. Firearm injuries in youth have notable health implications for the whole family, along with large effects on societal spending.

Effects of Remote Patient Monitoring Use on Care Outcomes Among Medicare Patients With Hypertension : An Observational Study.

BACKGROUND: Remote patient monitoring (RPM) is a promising tool for improving chronic disease management. Use of RPM for hypertension monitoring is growing rapidly, raising concerns about increased spending. However, the effects of RPM are still unclear. OBJECTIVE: To estimate RPM's effect on hypertension care and spending. DESIGN: Matched observational study emulating a longitudinal, cluster randomized trial. After matching, effect estimates were derived from a regression analysis comparing changes in outcomes from 2019 to 2021 for patients with hypertension at high-RPM practices versus those at matched control practices with little RPM use. SETTING: Traditional Medicare. PATIENTS: Patients with hypertension. INTERVENTION: Receipt of care at a high-RPM practice. MEASUREMENTS: Primary outcomes included hypertension medication use (medication fills, adherence, and unique medications received), outpatient visit use, testing and imaging use, hypertension-related acute care use, and total hypertension-related spending. RESULTS: 192 high-RPM practices (with 19 978 patients with hypertension) were matched to 942 low-RPM control practices (with 95 029 patients with hypertension). Compared with patients with hypertension at matched low-RPM practices, patients with hypertension at high-RPM practices had a 3.3% (95% CI, 1.9% to 4.8%) relative increase in hypertension medication fills, a 1.6% (CI, 0.7% to 2.5%) increase in days' supply, and a 1.3% (CI, 0.2% to 2.4%) increase in unique medications received. Patients at high-RPM practices also had fewer hypertension-related acute care encounters (-9.3% [CI, -20.6% to 2.1%]) and reduced testing use (-5.9% [CI, -11.9% to 0.0%]). However, these patients also saw increases in primary care physician outpatient visits (7.2% [CI, -0.1% to 14.6%]) and a $274 [CI, $165 to $384]) increase in total hypertension-related spending. LIMITATION: Lacked blood pressure data; residual confounding. CONCLUSION: Patients in high-RPM practices had improved hypertension care outcomes but increased spending. PRIMARY FUNDING SOURCE: National Institute of Neurological Disorders and Stroke.

Use of Telemedicine and Quality of Care Among Medicare Enrollees With Serious Mental Illness.

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.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Effectiveness of an inactivated SARS-CoV-2 vaccine in children and adolescents: a large-scale observational study.

Background: Policymakers urgently need evidence to adequately balance the costs and benefits of mass vaccination against COVID-19 across all age groups, including children and adolescents. In this study, we aim to assess the effectiveness of CoronaVac's primary series among children and adolescents in Chile. Methods: We used a large prospective national cohort of about two million children and adolescents 6-16 years to estimate the effectiveness of an inactivated SARS-CoV-2 vaccine (CoronaVac) in preventing laboratory-confirmed symptomatic SARS-CoV-2 infection (COVID-19), hospitalisation, and admission to an intensive care unit (ICU) associated with COVID-19. We compared the risk of individuals treated with a complete primary immunization schedule (two doses, 28 days apart) with the risk of unvaccinated individuals during the follow-up period. The study was conducted in Chile from June 27, 2021, to January 12, 2022, when the SARS-CoV-2 Delta variant was predominant but other variants of concern were co-circulating, including Omicron. We used inverse probability-weighted survival regression models to estimate hazard ratios of complete immunization over the unvaccinated status, accounting for time-varying vaccination exposure and adjusting for relevant demographic, socioeconomic, and clinical confounders. Findings: The estimated adjusted vaccine effectiveness for the inactivated SARS-CoV-2 vaccine in children aged 6-16 years was 74.5% (95% CI, 73.8-75.2), 91.0% (95% CI, 87.8-93.4), 93.8% (95% CI, 87.8-93.4) for the prevention of COVID-19, hospitalisation, and ICU admission, respectively. For the subgroup of children 6-11 years, the vaccine effectiveness was 75.8% (95% CI, 74.7-76.8) for the prevention of COVID-19 and 77.9% (95% CI, 61.5-87.3) for the prevention of hospitalisation. Interpretation: Our results suggest that a complete primary immunization schedule with the inactivated SARS-CoV-2 vaccine provides effective protection against severe COVID-19 disease for children 6-16 years. Funding: Agencia Nacional de Investigación y Desarrollo (ANID) Millennium Science Initiative Program and Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias (FONDAP).

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Examining sources of post-acute care inequities with layered target matching.

OBJECTIVE: To examine factors associated with racial inequities in discharge location, skilled nursing facility (SNF) utilization, and readmissions. DATA SOURCES: A 20% sample of longitudinal Medicare claims from 2016 to 2018. STUDY DESIGN: We present layered target matching, a method for studying sources of inequities. Layered target matching examines a fixed target population profile representing any race, ethnicity, or vulnerable population, sequentially adjusting for sets of characteristics that may contribute to inequities these groups endure. We use the method to study racial inequities in post-acute care use and readmissions. DATA COLLECTION/EXTRACTION METHODS: We studied Black and non-Hispanic White fee-for-service Medicare beneficiaries aged 66+ admitted to short-term acute-care hospitals for qualifying diagnoses or procedures between January 1, 2016 and November 30, 2018. PRINCIPAL FINDINGS: Admitted Black patients tended to be younger, had significantly higher rates of risk factors such as diabetes, stroke, or renal disease, and were much more frequently admitted to large or academic hospitals. Relative to demographically similar White patients, Black patients were significantly more likely to be discharged to SNFs (21.8% vs. 19.3%, difference = 2.5%, p < 0.0001) and to receive any SNF care within 30 days of discharge (25.3% vs. 22.4%, difference = 2.9%, p < 0.0001). Black patients were also significantly more likely to experience 30-day readmission (18.7% vs. 14.5%, difference = 4.2%, p < 0.0001). Differences in reasons for hospitalization and risk factors explained most of the differences in discharge location, post-acute care use, and readmission rates, while additional adjustment for differences in hospital characteristics and complications made little difference for any of the measures studied. CONCLUSIONS: We found significant Black-White differences in discharge to SNFs, SNF utilization, and readmission rates. Using layered target matching, we found that differences in risk factors and reasons for hospitalization explained most of these differences, while differences in hospitals did not materially impact the differences.

Profile Matching for the Generalization and Personalization of Causal Inferences.

We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible unweighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not always require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile comprises the characteristics of a single individual. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. The method can also be used for the selection of units for study follow-up, and it readily applies to multivalued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of the generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) the characteristics of a hypothetical vulnerable patient. The method can be implemented via the new function profmatch in the designmatch package for R, for which we provide a step-by-step tutorial.

The Appalachia Mind Health Initiative (AMHI): a pragmatic randomized clinical trial of adjunctive internet-based cognitive behavior therapy for treating major depressive disorder among primary care patients.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems. METHODS: Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE. DISCUSSION: The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT. TRIAL REGISTRATION: ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019.

Estimated Prevalence of and Factors Associated With Clinically Significant Anxiety and Depression Among US Adults During the First Year of the COVID-19 Pandemic.

Importance: Claims of dramatic increases in clinically significant anxiety and depression early in the COVID-19 pandemic came from online surveys with extremely low or unreported response rates. Objective: To examine trend data in a calibrated screening for clinically significant anxiety and depression among adults in the only US government benchmark probability trend survey not disrupted by the COVID-19 pandemic. Design, Setting, and Participants: This survey study used the US Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System (BRFSS), a monthly state-based trend survey conducted over the telephone. Participants were adult respondents in the 50 US states and District of Columbia who were surveyed March to December 2020 compared with the same months in 2017 to 2019. Exposures: Monthly state COVID-19 death rates. Main Outcomes and Measures: Estimated 30-day prevalence of clinically significant anxiety and depression based on responses to a single BRFSS item calibrated to a score of 6 or greater on the 4-item Patient Health Questionnaire (area under the receiver operating characteristic curve, 0.84). All percentages are weighted based on BRFSS calibration weights. Results: Overall, there were 1 429 354 respondents, with 1 093 663 in 2017 to 2019 (600 416 [51.1%] women; 87 153 [11.8%] non-Hispanic Black; 826 334 [61.5%] non-Hispanic White; 411 254 [27.8%] with college education; and 543 619 [56.8] employed) and 335 691 in 2020 (182 351 [51.3%] women; 25 517 [11.7%] non-Hispanic Black; 250 333 [60.5%] non-Hispanic White; 130 642 [29.3%] with college education; and 168 921 [54.9%] employed). Median within-state response rates were 45.9% to 49.4% in 2017 to 2019 and 47.9% in 2020. Estimated 30-day prevalence of clinically significant anxiety and depression was 0.4 (95% CI, 0.0 to 0.7) percentage points higher in March to December 2020 (12.4%) than March to December 2017 to 2019 (12.1%). This estimated increase was limited, however, to students (2.4 [95% CI, 0.8 to 3.9] percentage points) and the employed (0.9 [95% CI, 0.5 to 1.4] percentage points). Estimated prevalence decreased among the short-term unemployed (-1.8 [95% CI, -3.1 to -0.5] percentage points) and those unable to work (-4.2 [95% CI, -5.3 to -3.2] percentage points), but did not change significantly among the long-term unemployed (-2.1 [95% CI, -4.5 to 0.5] percentage points), homemakers (0.8 [95% CI, -0.3 to 1.9] percentage points), or the retired (0.1 [95% CI, -0.6 to 0.8] percentage points). The increase in anxiety and depression prevalence among employed people was positively associated with the state-month COVID-19 death rate (1.8 [95% CI, 1.2 to 2.5] percentage points when high and 0.0 [95% CI, -0.7 to 0.6] percentage points when low) and was elevated among women compared with men (2.0 [95% CI, 1.4 to 2.5] percentage points vs 0.2 [95% CI, -0.1 to 0.6] percentage points), Non-Hispanic White individuals compared with Hispanic and non-Hispanic Black individuals (1.3 [95% CI, 0.6 to 1.9] percentage points vs 1.1 [95% CI, -0.2 to 2.5] percentage points and 0.7 [95% CI, -0.1 to 1.5] percentage points), and those with college educations compared with less than high school educations (2.5 [95% CI, 1.9 to 3.1] percentage points vs -0.6 [95% CI, -2.7 to 1.4] percentage points). Conclusions and Relevance: In this survey study, clinically significant US adult anxiety and depression increased less during 2020 than suggested by online surveys. However, this modest aggregate increase could mask more substantial increases in key population segments (eg, first responders) and might have become larger in 2021 and 2022.

Survival outcomes following pregnancy or assisted reproductive technologies after breast cancer: A population-based study.

BACKGROUND: This study sought to determine the impact of pregnancy or assisted reproductive technologies (ART) on breast-cancer-specific survival among breast cancer survivors. METHODS: The authors performed a cohort study using a novel data linkage from the California Cancer Registry, the California birth cohort, and the Society for Assisted Reproductive Technology Clinic Outcome Reporting System data sets. They performed risk-set matching in women with stages I-III breast cancer diagnosed between 2000 and 2012. For each pregnant woman, comparable women who were not pregnant at that point but were otherwise similar based on observed characteristics were matched at the time of pregnancy. After matching, Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of pregnancy with breast-cancer-specific survival. We repeated these analyses for women who received ART. RESULTS: Among 30,021 women with breast cancer, 553 had a pregnancy and 189 attempted at least one cycle of ART. In Cox proportional hazards modeling, the pregnancy group had a higher 5-year disease-specific survival rate; 95.6% in the pregnancy group and 90.6% in the nonpregnant group (HR, 0.43; 95% CI, 0.24-0.77). In women with hormone receptor-positive cancer, we found similar results (HR, 0.43; 95% CI, 0.2-0.91). In the ART analysis, there was no difference in survival between groups; the 5-year disease-specific survival rate was 96.9% in the ART group and 94.1% in the non-ART group (HR, 0.44; 95% CI, 0.17-1.13). CONCLUSION: Pregnancy and ART are not associated with worse survival in women with breast cancer. LAY SUMMARY: We sought to determine the impact of pregnancy or assisted reproductive technologies (ART) among breast cancer survivors. We performed a study of 30,021 women by linking available data from California and the Society for Assisted Reproductive Technology Clinic Outcome Reporting System. For each pregnant woman, we matched at the time of pregnancy comparable women who were not pregnant at that point but were otherwise similar based on observed characteristics. We repeated these analyses for women who received ART. We found that pregnancy and ART were not associated with worse survival.

Effectiveness of CoronaVac in children 3-5 years of age during the SARS-CoV-2 Omicron outbreak in Chile.

The outbreak of the B.1.1.529 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Omicron) has caused an unprecedented number of Coronavirus Disease 2019 (COVID-19) cases, including pediatric hospital admissions. Policymakers urgently need evidence of vaccine effectiveness in children to balance the costs and benefits of vaccination campaigns, but, to date, the evidence is sparse. Leveraging a population-based cohort in Chile of 490,694 children aged 3-5 years, we estimated the effectiveness of administering a two-dose schedule, 28 days apart, of Sinovac's inactivated SARS-CoV-2 vaccine (CoronaVac). We used inverse probability-weighted survival regression models to estimate hazard ratios of symptomatic COVID-19, hospitalization and admission to an intensive care unit (ICU) for children with complete immunization over non-vaccination, accounting for time-varying vaccination exposure and relevant confounders. The study was conducted between 6 December 2021 and 26 February 2022, during the Omicron outbreak in Chile. The estimated vaccine effectiveness was 38.2% (95% confidence interval (CI), 36.5-39.9) against symptomatic COVID-19, 64.6% (95% CI, 49.6-75.2) against hospitalization and 69.0% (95% CI, 18.6-88.2) against ICU admission. The effectiveness against symptomatic COVID-19 was modest; however, protection against severe disease was high. These results support vaccination of children aged 3-5 years to prevent severe illness and associated complications and highlight the importance of maintaining layered protections against SARS-CoV-2 infection.

Effectiveness of homologous and heterologous booster doses for an inactivated SARS-CoV-2 vaccine: a large-scale prospective cohort study.

BACKGROUND: Several countries have authorised or begun using a booster vaccine dose against COVID-19. Policy makers urgently need evidence of the effectiveness of additional vaccine doses and its clinical spectrum for individuals with complete primary immunisation schedules, particularly in countries where the primary schedule used inactivated SARS-CoV-2 vaccines. METHODS: Using individual-level data, we evaluated a prospective, observational, national-level cohort of individuals (aged ≥16 years) affiliated with the Fondo Nacional de Salud insurance programme in Chile, to assess the effectiveness of CoronaVac (Sinovac Biotech), AZD1222 (Oxford-AstraZeneca), or BNT162b2 (Pfizer-BioNTech) vaccine boosters in individuals who had completed a primary immunisation schedule with CoronaVac, compared with unvaccinated individuals. Individuals administered vaccines from Feb 2, 2021, to the prespecified study end date of Nov 10, 2021, were evaluated; we excluded individuals with a probable or confirmed SARS-CoV-2 infection (RT-PCR or antigen test) on or before Feb 2, 2021, and individuals who had received at least one dose of any COVID-19 vaccine before Feb 2, 2021. We estimated the vaccine effectiveness of booster doses against laboratory-confirmed symptomatic COVID-19 (symptomatic COVID-19) cases and COVID-19 outcomes (hospitalisation, admission to the intensive care unit [ICU], and death We used inverse probability-weighted and stratified survival regression models to estimate hazard ratios, accounting for time-varying vaccination status and adjusting for relevant demographic, socioeconomic, and clinical confounders. We estimated the change in hazard from unvaccinated status to vaccinated status associated with the primary immunisation series and a booster vaccine. FINDINGS: 11 174 257 individuals were eligible for this study, among whom 4 127 546 completed a primary immunisation schedule (two doses) with CoronaVac and received a booster dose during the study period. 1 921 340 (46·5%) participants received an AZD1222 booster, 2 019 260 (48·9%) received a BNT162b2 booster, and 186 946 (4·5%) received a homologous booster with CoronaVac. We calculated an adjusted vaccine effectiveness (weighted stratified Cox model) in preventing symptomatic COVID-19 of 78·8% (95% CI 76·8-80·6) for a three-dose schedule with CoronaVac, 96·5% (96·2-96·7) for a BNT162b2 booster, and 93·2% (92·9-93·6) for an AZD1222 booster. The adjusted vaccine effectiveness against COVID-19-related hospitalisation, ICU admission, and death was 86·3% (83·7-88·5), 92·2% (88·7-94·6), and 86·7% (80·5-91·0) for a homologous CoronaVac booster, 96·1% (95·3-96·9), 96·2% (94·6-97·3), and 96·8% (93·9-98·3) for a BNT162b2 booster, and 97·7% (97·3-98·0), 98·9% (98·5-99·2), and 98·1% (97·3-98·6) for an AZD1222 booster. INTERPRETATION: Our results suggest that a homologous or heterologous booster dose for individuals with a complete primary vaccination schedule with CoronaVac provides a high level of protection against COVID-19, including severe disease and death. Heterologous boosters showed higher vaccine effectiveness than a homologous booster for all outcomes, providing additional support for a mix-and-match approach. FUNDING: Agencia Nacional de Investigación y Desarrollo through the Fondo Nacional de Desarrollo Científico y Tecnológico, Millennium Science Initiative Program, and Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias.

Changes in Health Care Spending, Use, and Clinical Outcomes After Nonfatal Firearm Injuries Among Survivors and Family Members : A Cohort Study.

BACKGROUND: Despite increasing awareness of firearm-related deaths, evidence on the clinical and economic implications of nonfatal firearm injuries is limited. OBJECTIVE: To measure changes in clinical and economic outcomes after nonfatal firearm injuries among survivors and their family members. DESIGN: Cohort study. SETTING: MarketScan Medicare and commercial claims data, 2008 to 2018. PARTICIPANTS: 6498 survivors of firearm injuries matched to 32 490 control participants and 12 489 family members of survivors matched to 62 445 control participants. INTERVENTION: Exposure to nonfatal firearm injury as a survivor or family member of a survivor. MEASUREMENTS: Changes in health care spending, use, and morbidity from preinjury through 1 year postinjury relative to control participants, on average and by type and severity of firearm injury. RESULTS: After nonfatal firearm injury, medical spending increased $2495 per person per month (402%) and cost sharing increased $102 per person per month (176%) among survivors relative to control participants (P < 0.001) in the first year after injury, driven by an increase in the first month of $25 554 (4122%) in spending and $1112 (1917%) in cost sharing per survivor (P < 0.001). All categories of health care use increased relative to the control group. Survivors had a 40% increase in pain diagnoses, a 51% increase in psychiatric disorders, and an 85% increase in substance use disorders after firearm injury relative to control participants (P < 0.001), accompanied by increased pain and psychiatric medications. Family members had a 12% increase in psychiatric disorders relative to their control participants (P = 0.003). These overall clinical and economic changes were driven by intentional firearm injuries and more severe firearm injuries. LIMITATION: Precision of diagnostic codes and generalizability to other patient populations, including Medicaid and uninsured patients. CONCLUSION: In survivors, nonfatal firearm injuries led to increases in psychiatric disorders, substance use disorders, and pain diagnoses, alongside substantial increases in health care spending and use. In addition, mental health worsened among family members. PRIMARY FUNDING SOURCE: National Institutes of Health.

Legislation Increased Medicare Telestroke Billing, But Underbilling And Erroneous Billing Remain Common.

In the Furthering Access to Stroke Telemedicine (FAST) Act, passed as part of a budget omnibus in 2018, Congress permanently expanded Medicare payment for telemedicine consultations for acute stroke ("telestroke") from delivery only in rural areas to delivery in both urban and rural areas, effective January 1, 2019. Using a controlled time-series analysis, we found that one year after FAST Act implementation, billing for Medicare telestroke increased substantially in emergency departments at both directly affected urban hospitals and indirectly affected rural hospitals. However, at that time only a minority of hospitals with known telestroke capacity had ever billed Medicare for that service, and there was substantial billing inconsistent with Medicare requirements. As Congress considers options for Medicare telemedicine payment after the COVID-19 pandemic, our findings, which are consistent with confusion among providers regarding telemedicine billing requirements, suggest that simplified payment rules would help ensure that expanded reimbursement achieves its intended impact.

Effectiveness of Localized Lockdowns in the COVID-19 Pandemic.

Nonpharmaceutical interventions, such as social distancing and lockdowns, have been essential to control of the coronavirus disease 2019 (COVID-19) pandemic. In particular, localized lockdowns in small geographic areas have become an important policy intervention for preventing viral spread in cases of resurgence. These localized lockdowns can result in lower social and economic costs compared with larger-scale suppression strategies. Using an integrated data set from Chile (March 3-June 15, 2020) and a novel synthetic control approach, we estimated the effect of localized lockdowns, disentangling its direct and indirect causal effects on transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our results showed that the effects of localized lockdowns are strongly modulated by their duration and are influenced by indirect effects from neighboring geographic areas. Our estimates suggest that extending localized lockdowns can slow down SARS-CoV-2 transmission; however, localized lockdowns on their own are insufficient to control pandemic growth in the presence of indirect effects from contiguous neighboring areas that do not have lockdowns. These results provide critical empirical evidence about the effectiveness of localized lockdowns in interconnected geographic areas.