The United States Department of Health and Human Services (HHS), is a cabinet-level executive branch department of the U.S. federal government created to protect the health of all Americans and providing essential human services. Its motto is "Improving the health, safety, and well-being of America". Before the separate federal Department of Education was created in 1979, it was called the Department of Health, Education, and Welfare (HEW).HHS is administered by the Secretary of Health and Human Services, who is appointed by the president with the advice and consent of the United States Senate. The position is currently held by Xavier Becerra.The United States Public Health Service Commissioned Corps, the uniformed service of the PHS, is led by the Surgeon General who is responsible for addressing matters concerning public health as authorized by the secretary or by the Assistant Secretary of Health in addition to his or her primary mission of administering the Commissioned Corps.S.S.S.S.S.S.S.S.S.S.
INTRODUCTION:Computer-coded verbal autopsy (CCVA) algorithms are routinely used to determine individual cause of death (COD) and derive population-level estimates of cause-specific mortality fractions (CSMFs). But frequent COD misclassification leads to biased CSMF estimates. The VA-calibration framework reduces the bias by estimating misclassification rates; but it overlooks systematic patterns and cross-country variation, reducing the accuracy of CSMF estimates. METHODS:Using CHAMPS (Child Health and Mortality Prevention Surveillance) data and the framework in Pramanik et al (2025), we estimate misclassification rates of three widely used CCVA algorithms (Expert Algorithm VA, InSilicoVA and InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight countries (Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa and 'other'). We then demonstrate their utility and use the Mozambique-specific rates to calibrate VA-only data from the Countrywide Mortality Surveillance for Action (COMSA) project in Mozambique. RESULTS:We report three key findings. First, the country-specific model better fits CHAMPS misclassification rates than the homogeneous model, reducing average absolute loss by 34%-38% for neonates and 13%-24% for children. Second, CCVA algorithms show consistent misclassification patterns, systematically overestimating or underestimating certain causes. Third, calibrating COMSA data increases neonatal CSMF for sepsis/meningitis/infection and decreases it for intrapartum-related events and prematurity; among children, CSMF increases for malaria and decreases for pneumonia. CONCLUSIONS:We present an inventory of VA misclassification rate estimates across two age groups, three CCVA algorithms and eight countries. These publicly available estimates enable the calibration of VA-only data from any country without needing access to CHAMPS data. More generally, these analyses reveal systematic algorithmic biases and highlight opportunities to refine future CCVA algorithms. As reliance on computer-coded and AI-driven approaches to COD determination grows, our integrated VA-calibration workflow, grounded in robust statistical frameworks and open-source software (misclassification matrix modeling, VA-calibration R package on GitHub and CRAN), offers a critical step towards improving the accuracy of mortality surveillance.
Importance:Although the recent proliferation of telemental health care has transformed the delivery of outpatient mental health care for many individuals in the US, little is known about how outpatients are distributed across telehealth, hybrid, and in-person care. Objective:To characterize the national distribution of sociodemographic and clinical outpatient mental health groups across telehealth, hybrid, and in-person mental health care. Design, Setting, and Participants:This was a cross-sectional analysis of all telehealth, hybrid, and all in-person mental health care by adults (aged ≥18 years) in the 2021-2022 Medical Expenditure Panel Survey (n = 4720). Data were analyzed from January to August 2025. Main Outcomes and Measures:Average annual percentages of adult mental health outpatients who used all telemental health care, hybrid, and all in-person mental health care were calculated overall and stratified by sociodemographic and clinical characteristics. Differences in percentages using each modality were evaluated by sociodemographic and clinical strata adjusted for age, sex, and distress level (Kessler-6 scale). Exposures:Type of mental health treatment used (telemental health, hybrid, or in-person). Results:The analysis involved 4720 participants (2235 aged 18-44 years; 3007 female). Approximate one-fourth (27.8%; 95% CI, 25.7-29.8) of mental health outpatients received all telemental health care, 21.5% (95% CI, 19.8-23.1) received hybrid care, and 50.6% (95% CI, 48.2-53.1) received all in-person care. The percentage of patients receiving all telemental health care was higher for younger (aged 18-44 years; 31.7%; 95% CI, 29.0-34.3) than middle age (aged 45-64 years; 24.2%; 95% CI, 21.1-27.4) or older (aged ≥65 years; 19.4%; 95% CI, 16.1-22.7) adults, high school (23.1% 95% CI, 20.4-25.8) and college (34.5%; 95% CI, 31.5-37.5) graduates than those without a high school diploma (19.9%; 95% CI, 13.7-26.1), patients with incomes >400% federal poverty level (33.8%; 95% CI, 30.9-36.7) than lower (range, 20.6% to 23.7%), private (30.8%; 95% CI, 28.5-33.1) than public (20.2%; 95% CI, 17.4-23.0) insurance, and urban (29.2%; 95% CI, 27.0-31.3) than rural (14.0%; 95% CI, 8.6-19.3) residence. Compared to patients receiving medication alone (15.4%; 95% CI, 12.5-18.3), those receiving psychotherapy with (25.9%; 95% CI, 23.2-28.6) or without (41.6%; 95% CI, 38.0-45.2) medication were more likely to use all telemental health. Patients with less than moderate distress (29.2%; 95% CI, 26.1-32.3) were also more likely than those with serious distress (21.2%; 95% CI, 16.7-25.6) to use all telemental health. In adjusted analyses, patients treated by mental health counselors (10.9%; 95% CI, 7.0-14.7) or social workers (8.4%; 95% CI, 4.1-12.7) were also more likely to receive all telemental health than were patients treated by other mental health clinicians. Conclusions and Relevance:The findings of this cross-sectional study indicate that telehealth has become a common means of receiving outpatient mental health care in the US, especially for resourced patients with less serious psychological distress who receive psychotherapy from mental health specialists.
Traditional vaccine clinical trials sample blood from all participants. In contrast, the test-negative immune correlates (TNIC) design only samples blood from participants who develop symptoms. We compared traditional to test-negative immune correlates methods in the mRNA-1273 severe acute respiratory syndrome coronavirus 2 vaccine efficacy clinical trial. Using a neutralizing antibody assay, hazard ratios were 0.48 (95% confidence interval [CI], .29–.73) and 0.55 (95% CI, .28–1.06) for traditional and test-negative methods, respectively. Analogous ratios for binding antibody assay were 0.69 (95% CI, .52–.94) and 0.78 (95% CI, .50–1.20). The results support use of the logistically simpler TNIC design.
BACKGROUND:We previously showed that ancestral-specific anti-Spike binding IgG concentration and 50% inhibitory dilution neutralizing antibody titer (nAb-ID50) measured at 2 weeks postdose 2 (∼peak) were inverse correlates of risk (CoRs) of COVID-19 over 2 months post ∼peak in the PREVENT-19 trial of the NVX-CoV2373 vaccine; there were not sufficient data to assess CoRs of severe COVID-19. METHODS:Here, we assessed, in the same vaccinated cohort, Delta- and ancestral-specific Spike IgG and nAb-ID50 at ∼peak and over time as CoRs of severe COVID-19 and of Delta COVID-19 over 3.5-10 months post ∼peak (287 breakthrough Delta cases, including 8 severe; 446 noncases). RESULTS:Peak antibody levels were much higher for noncases versus severe cases (all inferred Delta), with nAb-ID50 Delta geometric mean 209.5 arbitrary units (AU)/mL (95% CI: 176.1, 249.1) versus 9.6 AU/mL (95% CI: 2.4, 38.6), respectively. Frequency of detectable nAb-ID50 titer was 98.3% (97.2, 99.0) for noncases versus 62.5% (22.3, 93.9) for severe cases. All markers were inverse CoRs of severe COVID-19, with a ∼peak hazard ratio (HR) of 0.13 (95% CI: .03, .57) per 10-fold nAb-ID50 Delta increase. Severe COVID-19 risk through 305 days postday 35 was 0.0338 (0.0043, 0.206) at the nAb-ID50 Delta 2.5th percentile (8.4 AU/mL), and 0.002 (0.0000, 0.0108) and 0.0002 (0.0000, 0.0035) at the 50th and 95th percentiles (210, 2522 AU/mL). CONCLUSIONS:Postvaccination NVX-CoV2373 antibody levels are stronger predictors of severe COVID-19 than any-severity Delta COVID-19. Low antibody responses indicate vulnerability to severe COVID-19.
