Immigrants and refugees seeking admission to the United States must first undergo an overseas medical exam, overseen by the US Centers for Disease Control and Prevention (CDC), during which all persons ≥15 years old receive a chest x-ray to look for signs of tuberculosis. Although individual screening sites often implement quality control (QC) programs to ensure radiographs are interpreted correctly, the CDC does not currently have a method for conducting similar QC reviews at scale. We obtained digitized chest radiographs collected as part of the overseas immigration medical exam. Using radiographs from applicants 15 years old and older, we trained deep learning models to perform three tasks: identifying abnormal radiographs; identifying abnormal radiographs suggestive of tuberculosis; and identifying the specific findings (e.g., cavities or infiltrates) in abnormal radiographs. We then evaluated the models on both internal and external testing datasets, focusing on two classes of performance metrics: individual-level metrics, like sensitivity and specificity, and sample-level metrics, like accuracy in predicting the prevalence of abnormal radiographs. A total of 152,012 images (one image per applicant; mean applicant age 39 years) were used for model training. On our internal test dataset, our models performed well both in identifying abnormalities suggestive of TB (area under the curve [AUC] of 0.97; 95% confidence interval [CI]: 0.95, 0.98) and in estimating sample-level counts of the same (-2% absolute percentage error; 95% CIC: -8%, 6%). On the external test datasets, our models performed similarly well in identifying both generic abnormalities (AUCs ranging from 0.89 to 0.92) and those suggestive of TB (AUCs from 0.94 to 0.99). This performance was consistent across metrics, including those based on thresholded class predictions, like sensitivity, specificity, and F1 score. Strong performance relative to high-quality radiological reference standards across a variety of datasets suggests our models may make reliable tools for supporting chest radiography QC activities at CDC.

OBJECTIVES: To estimate the impact of occupational injury and illness on opioid-related mortality while accounting for confounding by preinjury opioid use. METHODS: We employed a retrospective cohort study design using Washington State workers' compensation data for 1994-2000 injuries linked to US Social Security Administration earnings and mortality data and National Death Index (NDI) cause of death data from 1994 to 2018. We categorised injuries as lost-time versus medical-only, where the former involved more than 3 days off work or permanent disability. We determined death status and cause of death from NDI records. We modelled separate Fine and Gray subdistribution hazard ratios (sHRs) and 95% CIs for injured men and women for opioid-related and all drug-related mortality through 2018. We used quantitative bias analysis to account for unmeasured confounding by preinjury opioid use. RESULTS: The hazard of opioid-related mortality was elevated for workers with lost-time relative to medical-only injuries: sHR for men: 1.53, 95% CI 1.41 to 1.66; for women: 1.31, 95% CI 1.16 to 1.48. Accounting for preinjury opioid use, effect sizes were reduced but remained elevated: sHR for men was 1.43, 95% simulation interval (SI) 1.20 to 1.69; for women: 1.27, 95% SI 1.10 to 1.45. CONCLUSIONS: Occupational injuries and illnesses severe enough to require more than 3 days off work are associated with an increase in the hazard of opioid-related mortality. The estimated increase is reduced when we account for preinjury opioid use, but it remains substantial. Reducing work-related injuries and postinjury opioid prescribing and improving employment and income security may decrease opioid-related mortality.

In 2021, the U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and state partners investigated a multi-state sample-initiated retrospective outbreak investigation (SIROI) consisting of a cluster of nine Salmonella Weltevreden illnesses associated with frozen, pre-cooked shrimp imported from India. Import surveillance testing identified Salmonella Weltevreden recovered from a cooked shrimp sample from Supplier B. In total, nine patients with clinical isolates highly related via whole genome sequencing were reported in four states with illness onset dates between February 26 and July 17, 2021. Epidemiologic data was gathered by state partners for seven patients, whom all reported exposure to shrimp. Five patients reported consuming shrimp cocktail from the same retailer. A traceback investigation for five of the six patients converged on Supplier B. This evidence demonstrated that the outbreak of Salmonella Weltevreden illnesses was caused by the consumption of cooked, ready-to-eat shrimp manufactured by Supplier B. At the time of the investigation, outbreak and recall information was shared with Indian competent authorities. In March 2022, a follow up inspection of Supplier B's facility in India was conducted, and insanitary conditions and practices were observed. This outbreak investigation highlighted the importance of multidisciplinary national and international public health partnerships. The lessons learned from this investigation should continue to inform investigational activities and food safety guidance for industry.

Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021-22 and 2022-23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble is the 2(nd) most accurate model measured by WIS in 2021-22 and the 5(th) most accurate in the 2022-23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change.

OBJECTIVE: To provide comprehensive epidemiological information about the distribution and occurrence of rabies during 2022 in the US, Canada, and Mexico. METHODS: The US National Rabies Surveillance System collected 2022 animal rabies data from US state and territorial public health departments and USDA Wildlife Services. Temporal and geographic analyses were conducted to evaluate trends in animal rabies cases. RESULTS: During 2022, 54 US jurisdictions reported 3,579 animal rabies cases, reflecting a 2.3% decline from 3,663 cases reported in 2021. Six states collectively reported > 50% of animal rabies cases: Texas (395 [11.0%]), Virginia (337 [9.4%]), Pennsylvania (329 [9.2%]), New York (267 [7.5%]), North Carolina (264 [7.4%]), and California (241 [6.7%]). Out of the total reported rabies animal cases, 3,234 (90.4%) were attributed to wildlife, with bats (1,218 [34.0%]), raccoons (1,014 [28.3%]), skunks (660 [18.4%]), and foxes (269 [7.5%]) representing the primary hosts confirmed with rabies. Rabid cats (222 [6.2%]), cattle (42 [1.2%]), and dogs (50 [1.4%]) constituted > 90% of reported domestic animal rabies cases. CONCLUSIONS: In 2022, there was an increase in the number of animal samples submitted for rabies testing in the US and Canada. A notable geographic expansion of gray fox rabies virus variant was detected in the US. Three human rabies deaths due to vampire bat rabies infection occurred in Mexico; none were reported from the US and Canada. CLINICAL RELEVANCE: Laboratory diagnosis of rabies in animals is critical to ensure judicious use of human rabies postexposure prophylaxis.

A healthcare provider unknowingly treated a patient with mpox and subsequently developed ocular mpox without rash. She breastfed during illness; her infant was not infected. This report addresses 3 challenges in mpox management and control: diagnosis in the absence of rash, exposures in healthcare settings, and management of lactating patients.

Invasive meningococcal disease (IMD), caused by infection with the bacterium Neisseria meningitidis, usually manifests as meningitis or septicemia and can be severe and life-threatening (1). Six serogroups (A, B, C, W, X, and Y) account for most cases (2). N. meningitidis is transmitted person-to-person via respiratory droplets and oropharyngeal secretions. Asymptomatic persons can carry N. meningitidis and transmit the bacteria to others, potentially causing illness among susceptible persons. Outbreaks can occur in conjunction with large gatherings (3,4). Vaccines are available to prevent meningococcal disease. Antibiotic prophylaxis for close contacts of infected persons is critical to preventing secondary cases (2).

We assessed syphilis screening data from overseas medical examinations among U.S.-bound refugees to characterize seropositive syphilis cases and treatment from January 1, 2015, to December 31, 2018. During this time period, all refugees 15 years and older were required to undergo syphilis screening prior to resettlement to the United States. Of the 160,381 refugee arrivals who had a syphilis screening performed, 697 (434 per 100,000) were diagnosed with any stage (infectious or non-infectious) of syphilis. Among the 697 persons with seropositive syphilis, a majority (63%) were from the Africa region and were male (58%), and 53 (7.6%) were diagnosed with an infectious stage of syphilis. All infectious cases were treated prior to resettlement. This information suggests a comparable risk of infection among U.S.-bound refugees compared to a report of syphilis among U.S.-bound refugees from 2009 to 2013, indicating low rates in this population for at least a decade.

BACKGROUND: SARS-CoV-2 infection in pregnancy is associated with an increased risk of adverse birth outcomes such as preterm birth, stillbirth, and maternal and infant complications. Previous research suggests an increased risk of severe COVID-19 illness and stillbirth in pregnant people during delta variant predominance in 2021; however, those studies did not assess timing of infection during pregnancy, and few of them described COVID-19 vaccination status. OBJECTIVE: Using a large population-based cohort, this study compared pregnancy and infant outcomes and described demographic and clinical characteristics of pregnant people with SARS-CoV-2 infection prior to and during the delta variant period. STUDY DESIGN: This retrospective cohort analysis included persons with confirmed SARS-CoV-2 infection in pregnancy from 6 US jurisdictions reporting to the Surveillance for Emerging Threats to Pregnant People and Infants Network. Data were collected through case reports of polymerase chain reaction-positive pregnant persons and linkages to birth certificates, fetal death records, and immunization records. We described clinical characteristics and compared frequency of spontaneous abortion (<20 weeks of gestation), stillbirth (≥20 weeks), preterm birth (<37 weeks), small for gestational age, and term infant neonatal intensive care unit admission between the time periods of pre-delta and delta variant predominance. Study time periods were determined by when variants constituted more than 50% of sequences isolated according to regional SARS-CoV-2 genomic surveillance data, with time periods defined for pre-delta (March 3, 2020-June 25, 2021) and Delta (June 26, 2021-December 25, 2021). Adjusted prevalence ratios were estimated for each outcome measure using Poisson regression and were adjusted for continuous maternal age, race and ethnicity, and insurance status at delivery. RESULTS: Among 57,563 pregnancy outcomes, 57,188 (99.3%) were liveborn infants, 65 (0.1%) were spontaneous abortions, and 310 (0.5%) were stillbirths. Most pregnant persons were unvaccinated at the time of SARS-CoV-2 infection, with a higher proportion in pre-delta (99.4%) than in the delta period (78.4%). Of those with infections during delta and who were previously vaccinated, the timing from last vaccination to infection was a median of 183 days. Compared to pre-delta, infections during delta were associated with a higher frequency of stillbirths (0.7% vs 0.4%; adjusted prevalence ratio, 1.55; 95% confidence interval, 1.14-2.09) and preterm births (12.8% vs 11.9%; adjusted prevalence ratio, 1.14; 95% confidence interval, 1.07-1.20). The delta period was associated with a lower frequency of neonatal intensive care unit admission (adjusted prevalence ratio, 0.74; 95% confidence interval, 0.67-0.82) than in the pre-delta period. During the delta period, infection during the third trimester was associated with a higher frequency of preterm birth (adjusted prevalence ratio, 1.41; 95% confidence interval, 1.28-1.56) and neonatal intensive care unit admission (adjusted prevalence ratio, 1.21; 95% confidence interval, 1.01-1.45) compared to the first and second trimester combined. CONCLUSION: In this US-based cohort of persons with SARS-CoV-2 infection in pregnancy, the majority were unvaccinated, and frequencies of stillbirth and preterm birth were higher during the delta variant predominance period than in the pre-delta period. During the delta period, frequency of preterm birth and neonatal intensive care unit admission was higher among infections occurring in the third trimester vs those earlier in pregnancy. These findings demonstrate population-level increases of adverse fetal and infant outcomes, specifically in the presence of a COVID-19 variant with more severe presentation.

BACKGROUND: Due to low numbers of active infections and persons presenting to health facilities for malaria treatment, case-based surveillance is inefficient for understanding the remaining disease burden in low malaria transmission settings. Serological data through the detection of IgG antibodies from previous malaria parasite exposure can fill this gap by providing a nuanced picture of where sustained transmission remains. Study enrollment at sites of gathering provides a potential approach to spatially estimate malaria exposure and could preclude the need for more intensive community-based sampling. METHODS: This study compared spatial estimates of malaria exposure from cross-sectional school- and community-based sampling in Haiti. A total of 52,405 blood samples were collected from 2012 to 2017. Multiplex bead assays (MBAs) tested IgG against P. falciparum liver stage antigen-1 (LSA-1), apical membrane antigen 1 (AMA1), and merozoite surface protein 1 (MSP1). Predictive geospatial models of seropositivity adjusted for environmental covariates, and results were compared using correlations by coordinate points and communes across Haiti. RESULTS: Consistent directional associations were observed between seroprevalence and environmental covariates for elevation (negative), air temperature (negative), and travel time to urban centers (positive). Spearman's rank correlation for predicted seroprevalence at coordinate points was lowest for LSA-1 (ρ = 0.10, 95% CI: 0.09-0.11), but improved for AMA1 (ρ = 0.36, 95% CI: 0.35-0.37) and MSP1 (ρ = 0.48, 95% CI: 0.47-0.49). CONCLUSIONS: In settings approaching P. falciparum elimination, case-based prevalence data does not provide a resolution of ongoing malaria transmission in the population. Immunogenic antigen targets (e.g., AMA1, MSP1) that give higher population rates of seropositivity provide moderate correlation to gold standard community sampling designs and are a feasible approach to discern foci of residual P. falciparum transmission in an area.

BACKGROUND: Cardiac function of critically ill patients with COVID-19 generally has been reported from clinically obtained data. Echocardiographic deformation imaging can identify ventricular dysfunction missed by traditional echocardiographic assessment. RESEARCH QUESTION: What is the prevalence of ventricular dysfunction and what are its implications for the natural history of critical COVID-19? STUDY DESIGN AND METHODS: This is a multicenter prospective cohort of critically ill patients with COVID-19. We performed serial echocardiography and lower extremity vascular ultrasound on hospitalization days 1, 3, and 8. We defined left ventricular (LV) dysfunction as the absolute value of longitudinal strain of < 17% or left ventricle ejection fraction (LVEF) of < 50%. Primary clinical outcome was inpatient survival. RESULTS: We enrolled 110 patients. Thirty-nine (35.5%) died before hospital discharge. LV dysfunction was present at admission in 38 patients (34.5%) and in 21 patients (36.2%) on day 8 (P = .59). Median baseline LVEF was 62% (interquartile range [IQR], 52%-69%), whereas median absolute value of baseline LV strain was 16% (IQR, 14%-19%). Survivors and nonsurvivors did not differ statistically significantly with respect to day 1 LV strain (17.9% vs 14.4%; P = .12) or day 1 LVEF (60.5% vs 65%; P = .06). Nonsurvivors showed worse day 1 right ventricle (RV) strain than survivors (16.3% vs 21.2%; P = .04). INTERPRETATION: Among patients with critical COVID-19, LV and RV dysfunction is common, frequently identified only through deformation imaging, and early (day 1) RV dysfunction may be associated with clinical outcome.

Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections.

In 2015, the Institute of Medicine (now the National Academy of Medicine) released the report Vital Signs: Core Metrics for Health and Health Care Progress as a “basic, minimum slate of core metrics for use as sentinel indices of performance at various levels with respect to the key elements of health and health care progress” (IOM, 2015). Although indicators of pediatric health were included in that report as key elements of healthy behaviors, healthy communities, and preventive services, the core measures in the report emphasized indicators of adult health. This series of papers, “Vital Signs for Pediatric Health”, describes four metrics across the pediatric life course, each measuring how well the health care system is building the physical, cognitive, and socio-emotional health of the pediatric population, thereby laying the foundation for life-long health and well-being. The metrics—infant mortality, school readiness, chronic absenteeism, and high school graduation—were selected to focus on four different developmental stages of growth. A standardized set of core metrics to assess pediatric health could provide data to support health systems in identifying important areas for attention among their pediatric population and enable them to respond in a timely way. This rapid response is especially important in pediatric health systems as children undergo rapid development within a short time span. | | This paper discusses one of those four measures—high school graduation—as a pediatric vital sign because it reflects more than a number. Successful completion of high school also reflects the degree to which healthy growth and development has been supported throughout childhood. Earning a high school diploma is a predictor of adult success, including better employment outcomes, better adult physical and mental health, and decreased likelihood of involvement with the criminal justice system (NCES, 2021b; Blackwell et al., 2014; Lochner and Moretti, 2004). Those who do not graduate from high school face a greater likelihood of health risks as adults, including lower life expectancy, limited employment prospects, lower lifetime wages, and increased risk of incarceration (APHA, 2018). | | The remainder of this paper defines high school graduation rate, the selection of the specific measure that can assess high school graduation, and the technical integrity of the measure. The paper also describes disparities by state, race and ethnicity, and socio-economic status and delineates the importance of a high school diploma, including impacts on employment, future earnings, and individual health. It is important to note that the General Equivalency Diploma (GED) is not considered in this paper. Research shows that adults with GEDs have health outcomes more similar to high school dropouts than to graduates and perform at the level of dropouts in the labor market (Zajacova, 2012; Heckman and LaFontaine, 2007). Finally, the paper lays out the challenges in linking education data with health systems data to help communities have a broader impact on improving the health and well-being of their populations, implementation challenges more broadly, and potential ways to improve use of this metric to increase high school graduation rates.

In 2015, the Institute of Medicine (now the National Academy of Medicine) released the report Vital Signs: Core Metrics for Health and Health Care Progress as a “basic, minimum slate of core metrics for use as sentinel indices of performance at various levels with respect to the key elements of health and health care progress” (IOM, 2015). Although indicators of pediatric health were included in that report as key elements of healthy behaviors, healthy communities, and preventive services, the core measures in the report emphasized indicators of adult health. This series of papers, “Vital Signs for Pediatric Health”, describes four metrics across the pediatric life course, each measuring how well the health care system is building the physical, cognitive, and socio-emotional health of the pediatric population, thereby laying the foundation for life-long health and well-being. The metrics—infant mortality, school readiness, chronic absenteeism, and high school graduation—were selected to focus on four different developmental stages of growth. A standardized set of core metrics to assess pediatric health could provide data to support health systems in identifying important areas for attention among their pediatric population and enable them to respond in a timely way. This rapid response is especially important in pediatric health systems as children undergo rapid development within a short time span. | | This paper discusses one of those four measures—chronic absenteeism in middle school—as a developmental target for middle childhood. Middle school may include grades 5 to 9 or a subset of these grades; typically, this includes children who are approximately 10–15 years old. While chronic absenteeism—typically defined as missing 10% or more of enrolled school days—has potential utility as a measure from the time children enter formal schooling in pre-kindergarten through grade 12, measuring chronic absenteeism in middle childhood provides critical information about health and well-being between two key developmental stages captured by other vital signs: early childhood, captured by school readiness, and adolescence, captured by high school graduation. | | Health and educational success are intertwined, as more education has been linked to better health outcomes across the life course (Basch, 2011; Cutler and Lleras-Muney, 2006). Therefore, participation in education is crucial for both developmental and educational progress. Research has documented associations of chronic absenteeism with both acute (e.g., influenza infection, fractures) and chronic (e.g., asthma, type 1 diabetes mellitus) physical health conditions, as well as with mental health disorders and substance use (Allison et al., 2019). Research has also linked chronic absenteeism to poorer educational performance and attainment (Allison et al., 2019). Chronic absenteeism is robustly inversely associated with on-time high school graduation (BERC, 2011). For more information on the importance of high school graduation, refer to “Vital Signs for Pediatric Health: High School Graduation” (Hoagwood et al., 2023). | | Given the links between educational attainment and health, measuring students' chronic absenteeism in middle school may provide a window into physical and mental health in middle childhood and early adolescence, socio-emotional functioning, readiness to learn, family risk and resilience, and potential threats to current and long-term health and well-being (RWJF, 2016). School-level chronic absenteeism, as well as aggregations of multiple schools, such as those served by a particular health system, may also provide an indicator of the adequacy of interventions to promote health and educational equity at the population level. As such, chronic absenteeism could be a vital sign to measure health systems' performance. | | The remainder of this paper defines the chronic absenteeism measure, the selection of the specific measure that assesses chronic absenteeism, and the technical integrity of the measure. The paper also makes clear the links between school attendance and individual health outcomes. The shifting landscape of chronic absenteeism in the COVID-19 global pandemic and its anticipated aftermath are also considered.

In 2015, the Institute of Medicine (now the National Academy of Medicine) released the report Vital Signs: Core Metrics for Health and Health Care Progress as a “basic, minimum slate of core metrics for use as sentinel indices of performance at various levels with respect to the key elements of health and health care progress” (IOM, 2015). Although indicators of pediatric health were included in that report as key elements of healthy behaviors, healthy communities, and preventive services, the core measures in the report emphasized indicators of adult health. This series of papers, “Vital Signs for Pediatric Health”, describes four metrics across the pediatric life course, each measuring how well the health care system is building the physical, cognitive, and socio-emotional health of the pediatric population, thereby laying the foundation for life-long health and well-being. The metrics—infant mortality, school readiness, chronic absenteeism, and high school graduation—were selected to focus on four different developmental stages of growth. A standardized set of core metrics to assess pediatric health could provide data to support health systems in identifying important areas for attention among their pediatric population and enable them to respond in a timely way. This rapid response is especially important in pediatric health systems as children undergo rapid development within a short time span. | | This paper discusses one of those four measures—school readiness—as a developmental target for early childhood (ages 0 to 5). School readiness could serve as a comprehensive lens for the early development of children across various domains: physical, socio-emotional, language, knowledge, cognition (including early literacy and math), and approaches to learning. Systemic measurement of young children’s school readiness plays a critical role at multiple levels of health and educational systems. At the individual level, comprehensive assessment in early childhood facilitates the early identification of children who would benefit from preventive and intervention services to support their growth and development. At the district level, aggregate indicators can identify where to target resources and interventions within local health and educational systems. At the systems level, understanding current trends in school readiness data can facilitate the development of effective state and federal performance indicators. Ultimately, longitudinal tracking of school readiness could monitor strengths, risks, and the effects of practice and policy changes on meeting the needs of young children over time across districts, states, and the country (Williams et al., 2019). Together, the role of school readiness in the broader picture of child health and development, as well as the identified roles for communities and systems to track school readiness data and intervene with programs and polices when indicated, support the utilization of school readiness as a pediatric vital sign. | | This paper outlines the authors’ definition of school readiness and describes why it is a promising pediatric vital sign. The paper also outlines the importance of school readiness and its impacts on individual health, as well as how a lack of school readiness can lead to health disparities. Finally, the paper lays out the challenges in linking data across health and educational systems to further the capability of communities in improving the health and well-being of their populations.

In 2015, the Institute of Medicine (now the National Academy of Medicine) released the report Vital Signs: Core Metrics for Health and Health Care Progress as a “basic, minimum slate of core metrics for use as sentinel indices of performance at various levels with respect to the key elements of health and health care progress” (IOM, 2015). Although indicators of pediatric health were included in that report as key elements of healthy behaviors, healthy communities, and preventive services, the core measures in the report emphasized indicators of adult health. This series of papers, “Vital Signs for Pediatric Health”, describes four metrics across the pediatric life course, each measuring how well the health care system is building the physical, cognitive, and socio-emotional health of the pediatric population, thereby laying the foundation for life-long health and well-being. The metrics—infant mortality, school readiness, chronic absenteeism, and high school graduation—were selected to focus on four different developmental stages of growth. A standardized set of core metrics to assess pediatric health could provide data to support health systems in identifying important areas for attention among their pediatric population and enable them to respond in a timely way. This rapid response is especially important in pediatric health systems as children undergo rapid development within a short time span. | | This paper discusses one of those four measures—infant mortality rate (IMR)—as a measure of the early period of life. IMR is used globally as a key indicator of child survival. As a measure, it is sensitive to improvements in care for both women and children, is associated with other well-being indicators in communities, and reflects health inequities in the community (ChildStats, 2021; Ely et al., 2017). | | This paper defines IMR and describes why it is a critical pediatric vital sign, then articulates the value of reducing disparities in IMR. Finally, the paper lays out the challenges to using IMR as an indicator for health system accountability.

Leptospirosis, the most widespread zoonotic disease in the world, is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 266 sampled opportunistically from other California regions and species. In the five target species seroprevalences ranged from 5 to 60%, and infection prevalences ranged from 0.8 to 15.2% in all except fox squirrels (0%). Leptospira phylogenomics and patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.Competing Interest StatementAV, MC, and APP report grants from Metabiota Inc outside the submitted work.Funding StatementFor teams that reported receiving funding for their work, we report the sources and disclosures below. CMU-TimeSeries: CDC Center of Excellence, gifts from Google and Facebook. CU-select: NSF DMS-2027369 and a gift from the Morris-Singer Foundation. COVIDhub: This work has been supported by the US Centers for Disease Control and Prevention (1U01IP001122) and the National Institutes of General Medical Sciences (R35GM119582). The content is solely the responsibility of the authors and does not necessarily represent the official views of CDC, NIGMS or the National Institutes of Health. Johannes Bracher was supported by the Helmholtz Foundation via the SIMCARD Information&amp; Data Science Pilot Project. Tilmann Gneiting gratefully acknowledges support by the Klaus Tschira Foundation. DDS-NBDS: NSF III-1812699. EPIFORECASTS-ENSEMBLE1: Wellcome Trust (210758/Z/18/Z) GT_CHHS-COVID19: William W. George Endowment, Virginia C. and Joseph C. Mello Endowments, NSF DGE-1650044, NSF MRI 1828187, research cyberinfrastructure resources and services provided by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech, and the following benefactors at Georgia Tech: Andrea Laliberte, Joseph C. Mello, Richard Rick E. &amp; Charlene Zalesky, and Claudia &amp; Paul Raines GT-DeepCOVID: CDC MInD-Healthcare U01CK000531-Supplement. NSF (Expeditions CCF-1918770, CAREER IIS-2028586, RAPID IIS-2027862, Medium IIS-1955883, NRT DGE-1545362), CDC MInD program, ORNL and funds/computing resources from Georgia Tech and GTRI. IHME: This work was supported by the Bill &amp; Melinda Gates Foundation, as well as funding from the state of Washington and the National Science Foundation (award no. FAIN: 2031096). IowaStateLW-STEM: Iowa State University Plant Sciences Institute Scholars Program, NSF DMS-1916204, NSF CCF-1934884, Laurence H. Baker Center for Bioinformatics and Biological Statistics. JHU_IDD-CovidSP: State of California, US Dept of Health and Human Services, US Dept of Homeland Security, US Office of Foreign Disaster Assistance, Johns Hopkins Health System, Office of the Dean at Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University Modeling and Policy Hub, Centers fo Disease Control and Prevention (5U01CK000538-03), University of Utah Immunology, Inflammation, &amp; Infectious Disease Initiative (26798 Seed Grant). LANL-GrowthRate: LANL LDRD 20200700ER. MOBS-GLEAM_COVID: COVID Supplement CDC-HHS-6U01IP001137-01. NotreDame-mobility and NotreDame-FRED: NSF RAPID DEB 2027718 UA-EpiCovDA: NSF RAPID Grant # 2028401. UCSB-ACTS: NSF RAPID IIS 2029626. UCSD-NEU: Google Faculty Award, DARPA W31P4Q-21-C-0014, COVID Supplement CDC-HHS-6U01IP001137-01. UMass-MechBayes: NIGMS R35GM119582, NSF 1749854. UMich-RidgeTfReg: The University of Michigan Physics Department and the University of Michigan Office of Research.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:UMass-Amherst IRBAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAll data and code referred to in the manuscript are publicly available. https://github.com/reichlab/covid19-forecast-hub/ https://github.com/reichlab/covidEnsembles https://zoltardata.com/project/44

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach.

Leptospirosis is the most widespread zoonotic disease in the world, yet it is broadly understudied in multi-host wildlife systems. Knowledge gaps regarding Leptospira circulation in wildlife, particularly in densely populated areas, contribute to frequent misdiagnoses in humans and domestic animals. We assessed Leptospira prevalence levels and risk factors in five target wildlife species across the greater Los Angeles region: striped skunks (Mephitis mephitis), Northern raccoons (Procyon lotor), coyotes (Canis latrans), Virginia opossums (Didelphis virginiana), and fox squirrels (Sciurus niger). We sampled more than 960 individual animals, including over 700 from target species in the greater Los Angeles region, and an additional 260 sampled opportunistically from other regions and species. In the five target species, seroprevalences ranged from 5-60% and active infection prevalences ranged from 0.8-15.2% in all except fox squirrels (0%). Patterns of serologic reactivity suggest that mainland terrestrial wildlife, particularly mesocarnivores, could be the source of repeated observed introductions of Leptospira into local marine and island ecosystems. Overall, we found evidence of widespread Leptospira exposure in wildlife across Los Angeles and surrounding regions. This indicates exposure risk for humans and domestic animals and highlights that this pathogen can circulate endemically in many wildlife species even in densely populated urban areas. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Cryptosporidium canis is a zoonotic species causing cryptosporidiosis in humans in addition to its natural hosts dogs and other fur animals. To understand the genetic basis for host adaptation, we sequenced the genomes of C. canis from dogs, minks, and foxes and conducted a comparative genomics analysis. While the genomes of C. canis have similar gene contents and organisations, they (~41.0 %) and C. felis (39.6 %) have GC content much higher than other Cryptosporidium spp. (24.3-32.9 %) sequenced to date. The high GC content is mostly restricted to subtelomeric regions of the eight chromosomes. Most of these GC-balanced genes encode Cryptosporidium-specific proteins that have intrinsically disordered regions and are involved in host-parasite interactions. Natural selection appears to play a more important role in the evolution of codon usage in GC-balanced C. canis, and most of the GC-balanced genes have undergone positive selection. While the identity in whole genome sequences between the mink- and dog-derived isolates is 99.9 % (9365 SNVs), it is only 96.0 % (362 894 SNVs) between them and the fox-derived isolate. In agreement with this, the fox-derived isolate possesses more subtelomeric genes encoding invasion-related protein families. Therefore, the change in subtelomeric GC content appears to be responsible for the more GC-balanced C. canis genomes, and the fox-derived isolate could represent a new Cryptosporidium species.

In 2020, approximately 21.5 million employed U.S. adults aged 18-64 years had some form of disability. Although 75.8% of noninstitutionalized persons without disability aged 18-64 were employed, only 38.4% of their counterparts with disability were employed (1). Persons with disability have job preferences similar to persons without disability but might encounter barriers (e.g., lower average training or education levels, discrimination, or limited transportation options) that affect the types of jobs they hold (2,3). CDC analyzed 2016-2020 Behavioral Risk Factor Surveillance System (BRFSS) data from 35 states and Guam to estimate disability prevalences, by type and occupation group, among currently employed U.S. adults aged 18-64 years. The highest adjusted disability prevalences were among workers in three of the 22 major occupation groups: food preparation and serving-related (19.9%); personal care and service (19.4%); and arts, design, entertainment, sports, and media (17.7%). Occupation groups with the lowest adjusted disability prevalences were business and financial operations (11.3%), health care practitioners and technicians (11.1%), and architecture and engineering (11.0%). The distributions of persons with and without disability differ across occupations. Workplace programs that address the training, education, and workplace needs of employees with disability might improve workers' ability to enter, thrive in, and advance in a wider range of occupations.

Keeping the global food supply safe necessitates international collaborations between countries. Health and regulatory agencies routinely communicate during foodborne illness outbreaks, allowing partners to share investigational evidence. A 2016-2020 outbreak of Listeria monocytogenes infections linked to imported enoki mushrooms required a multinational collaborative investigation among the United States, Canada, Australia, and France. Ultimately, this outbreak included 48 ill people, 36 in the United States and 12 in Canada, and was linked to enoki mushrooms sourced from one manufacturer located in the Republic of Korea. Epidemiologic, laboratory, and traceback evidence led to multiple regulatory actions, including extensive voluntary recalls by three firms in the United States and one firm in Canada. In the United States and Canada, the Korean manufacturer was placed on import alert while other international partners provided information about their respective investigations and advised the public not to eat the recalled enoki mushrooms. The breadth of the geographic distribution of this outbreak emphasizes the global reach of the food industry. This investigation provides a powerful example of the impact of national and international coordination of efforts to respond to foodborne illness outbreaks and protect consumers. It also demonstrates the importance of fast international data sharing and collaboration in identifying and stopping foodborne outbreaks in the global community. Additionally, it is a meaningful example of the importance of food sampling, testing, and integration of sequencing results into surveillance databases.

Objectives. To examine the potential impact of contact tracing to identify contacts and prevent mpox transmission among gay, bisexual, and other men who have sex with men (MSM) as the outbreak expanded. Methods. We assessed contact tracing outcomes from 10 US jurisdictions before and after access to the mpox vaccine was expanded from postexposure prophylaxis for persons with known exposure to include persons at high risk for acquisition (May 17-June 30, 2022, and July 1-31, 2022, respectively). Results. Overall, 1986 mpox cases were reported in MSM from included jurisdictions (240 before expanded vaccine access; 1746 after expanded vaccine access). Most MSM with mpox were interviewed (95.0% before vaccine expansion and 97.0% after vaccine expansion); the proportion who named at least 1 contact decreased during the 2 time periods (74.6% to 38.9%). Conclusions. During the period when mpox cases among MSM increased and vaccine access expanded, contact tracing became less efficient at identifying exposed contacts. Public Health Implications. Contact tracing was more effective at identifying persons exposed to mpox in MSM sexual and social networks when case numbers were low, and it could be used to facilitate vaccine access. (Am J Public Health. Published online ahead of print May 4, 2023:e1-e4. https://doi.org/10.2105/AJPH.2023.307301).

Introduction: As the proportion of males with Duchenne muscular dystrophy (DMD) surviving into adulthood increases, more information is needed regarding their health care transition planning, an essential process for adolescents and young adults with DMD. The objective of this study was to describe the health care transition experiences of a population of males living with Duchenne or Becker muscular dystrophy (DBMD). Methods: The eligible participants, identified through the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet) surveillance project, were 16-31 years old and lived in Arizona, Colorado, Georgia, Iowa, or western New York (n=258). The MD STARnet Health Care Transitions and Other Life Experiences Survey was conducted in 2013 and administered online or in a telephone interview. Sixty-five males (25%) completed the survey. Among non-ambulatory males, response differences were compared by age group. Statistical comparisons were conducted using Fisher's exact test, or when appropriate, the Chisquare test. Results: Twenty-one percent of non-ambulatory males aged 16-18 years, 28% of non-ambulatory males aged 19-23 years, 25% of non-ambulatory males aged 24-30 years, and 18 ambulatory males had a written transition plan. Nineteen percent of non-ambulatory males aged 24-30 years had delayed or gone without needed health care in the past 12 months. Among non-ambulatory males aged 24-30 years, 75% had cardiology providers and 69% had pulmonology providers involved in their care in the past 12 months. Twentyeight percent of non-ambulatory males aged 19-23 years and 25% of non-ambulatory males aged 24-30 years reported that they did not receive health care or other services at least once because they were unable to leave their home. Non-ambulatory males aged 16-18 years (29%) were less likely to have ever discussed how to obtain or keep health insurance as they get older compared to non-ambulatory males aged 24-30 years (69%) (p <0.01). Discussion: This study identified potential barriers to the successful health care transition of males with DBMD. The results of this study may indicate a lack of targeted informational resources and education focused on supporting the transition of young men with DBMD as they age from adolescence into adulthood within the healthcare system. Future studies could determine the reasons for the potential barriers to health care and identify the optimal transition programs for males with DBMD. There are a few online resources on transition available to adolescents and young adults with special health care needs.

This article summarizes what is currently known about the 2019 novel coronavirus and offers interim guidance.

INTRODUCTION: External Quality Assessment (EQA) schemes are designed to provide a snapshot of laboratory proficiency, identifying issues and providing feedback to improve laboratory performance and inter-laboratory agreement in testing. Currently there are no international EQA schemes for seasonal influenza serology testing. Here we present a feasibility study for conducting an EQA scheme for influenza serology methods. METHODS: We invited participant laboratories from industry, contract research organizations (CROs), academia and public health institutions who regularly conduct hemagglutination inhibition (HAI) and microneutralization (MN) assays and have an interest in serology standardization. In total 16 laboratories returned data including 19 data sets for HAI assays and 9 data sets for MN assays. RESULTS: Within run analysis demonstrated good laboratory performance for HAI, with intrinsically higher levels of intra-assay variation for MN assays. Between run analysis showed laboratory and strain specific issues, particularly with B strains for HAI, whilst MN testing was consistently good across labs and strains. Inter-laboratory variability was higher for MN assays than HAI, however both assays showed a significant reduction in inter-laboratory variation when a human sera pool is used as a standard for normalization. DISCUSSION: This study has received positive feedback from participants, highlighting the benefit such an EQA scheme would have on improving laboratory performance, reducing inter laboratory variation and raising awareness of both harmonized protocol use and the benefit of biological standards for seasonal influenza serology testing.

BACKGROUND: AND SETTING: From 2015-2020 the THRIVE project supported seven U.S. health departments to improve HIV prevention services for Black or African American (Black) and Hispanic or Latino gay, bisexual, and other men who have sex with men (GBM) and transgender women (TGW). METHODS: We described services provided in the THRIVE PrEP continuum. Using Poisson regression models we estimated associations between race or ethnicity and age and PrEP screening, linkage, and prescription. We examined associations between co-location of services and PrEP linkage and prescription for two sites. RESULTS: THRIVE served 12,972 GBM without HIV; 37% of PrEP-eligible GBM were prescribed PrEP. THRIVE served 1,185 TGW without HIV; 45% of PrEP-eligible TGW were prescribed PrEP. Black and Hispanic or Latino GBM were 29% (RR=0.71, 95% CI 0.66-0.77) and 19% (RR=0.81, 95% CI 0.75-0.87) less likely, respectively, to be prescribed PrEP than White GBM. GBM 18-24 years and ≥55 years were 19% (RR=0.81, 95% CI 0.75-0.87) and 22% (RR=0.78, 95% CI 0.67-0.9) less likely, respectively, to be prescribed PrEP compared to those 35-44 years. Co-located services were associated with a 54% (RR = 1.54, 95% CI 1.44-1.64) and a 31% (RR = 1.31, 95% CI: 1.19-1.43) greater likelihood of PrEP linkage and prescription respectively compared to services at different locations. CONCLUSIONS: THRIVE provided PrEP to higher proportions of PrEP-eligible persons than current national estimates, however PrEP use disparities persist. Co-location of services may be a useful component of jurisdictional strategies to increase PrEP coverage.

IgG serology can be utilized to estimate exposure to Anopheline malaria vectors and the Plasmodium species they transmit. A multiplex bead-based assay simultaneously detected IgG to Anopheles albimanus salivary gland extract (SGE) and four Plasmodium falciparum antigens (CSP, LSA-1, PfAMA1, and PfMSP1) in 11,541 children enrolled at 350 schools across Haiti in 2016. Logistic regression estimated odds of an above-median anti-SGE IgG response adjusting for individual- and environmental-level covariates. Spatial analysis detected statistically significant clusters of schools with students having high anti-SGE IgG levels, and spatial interpolation estimated anti-SGE IgG levels in unsampled locations. Boys had 11% (95% CI: 0.81, 0.98) lower odds of high anti-SGE IgG compared to girls, and children seropositive for PfMSP1 had 53% (95% CI: 1.17, 2.00) higher odds compared to PfMSP1 seronegatives. Compared to the lowest elevation, quartiles 2-4 of higher elevation were associated with successively lower odds (0.81, 0.43, and 0.34, respectively) of high anti-SGE IgG. Seven significant clusters of schools were detected in Haiti, while spatially interpolated results provided a comprehensive picture of anti-SGE IgG levels in the study area. Exposure to malaria vectors by IgG serology with SGE is a proxy to approximate vector biting in children and identify risk factors for vector exposure.