Metal exposure is a potential risk factor for amyotrophic lateral sclerosis (ALS). Increasing evidence suggests that elevated levels of DNA damage are present in both familial (fALS) and sporadic (sALS) forms of ALS, characterized by the selective loss of motor neurons in the brain, brainstem, and spinal cord. However, identifying and differentiating initial biomarkers of DNA damage response (DDR) in both forms of ALS remains unclear. The toxicological profiles from the Agency for Toxic Substances and Disease Registry (ATSDR) and our previous studies have demonstrated the influence of metal exposure-induced genotoxicity and neurodegeneration. A comprehensive overview of the ATSDR's toxicological profiles and the available literature identified 15 metals (aluminum (Al), arsenic (As), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), mercury (Hg), nickel (Ni), selenium (Se), uranium (U), vanadium (V), and zinc (Zn)) showing exposure-induced genotoxicity indicators associated with ALS pathogenesis. Genetic factors including mutations seen in ALS types and with concomitant metal exposure were distinguished, showing that heavy metal exposure can exacerbate the downstream effect of existing genetic mutations in fALS and may contribute to motor neuron degeneration in sALS. Substantial evidence associates heavy metal exposure to genotoxic endpoints in both forms of ALS; however, a data gap has been observed for several of these endpoints. This review aims to (1) provide a comprehensive overview of metal exposure-induced genotoxicity in ALS patients and experimental models, and its potential role in disease risk, (2) summarize the evidence for DNA damage and associated biomarkers in ALS pathogenesis, (3) discuss possible mechanisms for metal exposure-induced genotoxic contributions to ALS pathogenesis, and (4) explore the potential distinction of genotoxic biomarkers in both forms of ALS. Our findings support the association between metal exposure and ALS, highlighting under or unexplored genotoxic endpoints, signaling key data gaps. Given the high prevalence of sALS and studies showing associations with environmental exposures, understanding the mechanisms and identifying early biomarkers is vital for developing preventative therapies and early interventions. Limitations include variability in exposure assessment and the complexity of gene-environment interactions. Studies focusing on longitudinal exposure assessments, mechanistic studies, and biomarker identification to inform preventative and therapeutic strategies for ALS is warranted.

Ensuring prompt and effective case management of malaria remains an ongoing challenge in Zambia, where care is not sought for roughly 40% of febrile children under 5 years of age. To expand access, the Ministry of Health has scaled up routine malaria community case management (mCCM) for all ages over the past decade. As of 2018, nearly a quarter of children who received antimalarials obtained them from a community health worker (CHW), but gaps in treatment seeking remain. Proactive community case management (proCCM), under which CHWs regularly visit households to screen, test and treat individuals for malaria, aims to improve timely case management, avert severe disease and potentially reduce transmission. To evaluate the impact of weekly proCCM on malaria parasite prevalence and incidence in the context of strong routine community case management, we conducted a two-arm cluster-randomised controlled trial, comparing proCCM plus routine passive care to routine passive care only in Chadiza District, Eastern Province, Zambia, between April 2021 and May 2023. Baseline and endline surveys were conducted during peak transmission season to ascertain parasite prevalence, while facility, routine mCCM and proCCM incidence data were collected through routine surveillance systems and weekly household visits, respectively. In the control arm, malaria prevalence decreased from 19.7% in 2021 to 16.0% in 2023, and in the intervention arm, from 18.7% to 13.7%. No significant difference between arms in the change in parasite prevalence was estimated (adjusted relative risk=0.97, 95% CI=0.77 to 1.23). However, there was a small, ongoing decline in malaria incidence each month in proCCM clusters compared with control clusters (adjusted incidence rate ratio=0.98, 95% Bayesian credible interval=0.96 to 0.99). Our study suggests proCCM may modestly reduce malaria incidence over time in some settings with high baseline utilisation of routine facility and community case management. Trial registration number: NCT04839900.

BACKGROUND: Many medications can have blood pressure (BP)-elevating effects, which might negatively impact BP control among people with hypertension. This study examines trends in prescription fills for BP-elevating and antihypertensive medications, individually and concurrently, among US individuals. METHODS: Quarterly trends of concurrent and individual fills for BP-elevating and antihypertensive medications were reported using the nationwide sample from IQVIA's Total Patient Tracker database, covering 94% of all retail prescription fills in the United States. We identified 1387 products containing BP-elevating medications and 240 products containing antihypertensive medications. Percentage change from Q1/2017 and average quarterly percent change from the joinpoint regression were used to present trends overall and by sex and age group (0-17, 18-44, 45-64, 65-74, and ≥75 years). RESULTS: From 2017 to 2023, fills remained stable for BP-elevating medications alone and increased for antihypertensive medications alone (9.5% increase; from 10.1% to 11.0%; P<0.001). Concurrent fills for antihypertensive and BP-elevating medications increased by 15.9% (from 5.4% to 6.2%; P<0.001). Fills for BP-elevating medications were higher among adult women compared with men; among women aged 18 to 44 years, this was primarily due to the use of combined oral contraceptives. In Q4/2023, fills for BP-elevating medications were most common among those aged 65 to 74 years (females=30.7%; males=20.4%). CONCLUSIONS: These results provide the first nationwide trends in concurrent prescription fills for BP-elevating and antihypertensive medications, indicating an increasing trend. Our findings might inform clinician decision-making regarding medication selection for patients with hypertension.

In 2006, human papillomavirus (HPV) vaccine was first recommended in the United States to prevent cancers and other diseases caused by HPV; vaccination coverage increased steadily through 2021, and increasing numbers of young women had received HPV vaccine as children or adolescents. Since 2008, CDC has monitored incidence of precancerous lesions (cervical intraepithelial neoplasia [CIN] grades 2-3 and adenocarcinoma in situ [AIS], collectively CIN2+), which are detected through cervical cancer screening and can be used as an intermediate outcome for monitoring vaccination impact, via the five-site Human Papillomavirus Vaccine Impact Monitoring Project. This analysis describes trends in incidence of CIN2+ and CIN3+ (i.e., CIN grade 3 and AIS) lesions during 2008-2022. Among women aged 20-24 years who were screened for cervical cancer, rates during 2008-2022 decreased for CIN2+ by 79%, and for CIN3+ by 80%. In the same period, CIN3+ rates among screened women aged 25-29 years decreased by 37%. These data are consistent with considerable impact of HPV vaccination for preventing cervical precancers among women in the age groups most likely to have been vaccinated, and support existing recommendations to vaccinate children at the routinely recommended ages as a cancer prevention measure.

Antimalarial therapeutic efficacy studies are vital for monitoring drug efficacy in malaria-endemic regions. The WHO recommends genotyping polymorphic markers including msp-1, msp-2, and glurp for distinguishing recrudescences from reinfections. Recently, WHO proposed replacing glurp with microsatellites (Poly-α, PfPK2, TA1). However, suitable combinations with msp-1 and msp-2, as well as the performance of different algorithms for classifying recrudescence, have not been systematically assessed. This study investigated various microsatellites alongside msp-1 and msp-2 for molecular correction and compared different genotyping algorithms across three sites in Uganda. Microsatellites 313, Poly-α, and 383 exhibited the highest diversity, while PfPK2 and Poly-α revealed elevated multiplicity of infection (MOI) across all sites. The 3/3 match-counting algorithm classified significantly fewer recrudescences than both the ≥ 2/3 and Bayesian algorithms at probability cutoffs of ≥ 0.7 and ≥ 0.8 (P < 0.05). The msp-1/msp-2/2490 combination identified more recrudescences using the ≥ 2/3 and 3/3 algorithms in the artemether-lumefantrine (AL) treatment arm, while msp-1/msp-2/glurp combination classified more cases of recrudescence using the ≥ 2/3 in the dihydroartemisinin-piperaquine (DP) arm. Microsatellites PfPK2 and Poly-α, potentially sensitive to detecting minority clones, are promising replacements for glurp. Discrepancies in recrudescence classification between match-counting and Bayesian algorithms highlight the need for standardized PCR correction practices.

INTRODUCTION: Despite progress in systemic lupus erythematosus (SLE) treatment, challenges persist in medication adherence due to side effects and costs. Precision nutrition, particularly adjusting fatty acid intake, offers a cost-effective strategy for enhancing SLE management. Prior research, including our own, indicates that increased consumption of omega-3 polyunsaturated fatty acids (PUFAs) correlates with improved outcomes in SLE patients. Here we build upon these findings by investigating associations between serum fatty acids-grouped as PUFAs, monounsaturated fatty acids (MUFAs), and saturated fatty acids (SFAs)-and lupus activity, pain, and sleep disturbance. METHODS: Using data from 418 participants with SLE in the Michigan Lupus Epidemiology and Surveillance (MILES) Cohort, we examined associations between serum levels of 25 fatty acids determined by GC-MS and patient-reported outcomes. Disease activity, pain, and sleep quality were assessed using standardized questionnaires. Generalized additive models and partial residual plots were utilized to examine the linearity of fatty acid effects. Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO), followed by multiple linear regression adjusting for sociodemographic factors. RESULTS: Findings indicated favorable associations between ω-3 PUFAs-and, to a lesser extent, ω-6 PUFAs-and patient-reported outcomes, while MUFAs and SFAs showed unfavorable associations. Docosahexaenoic acid (DHA), an omega-3 PUFA, exhibited the most robust favorable associations across all outcomes. Additionally, the omega-3 α-linolenic acid (ALA) was linked to reduced pain, whereas eicosapentaenoic acid (EPA), another omega-3, was associated with worsened disease activity and pain. Among omega-6 PUFAs, dihomo-γ-linolenic acid (DGLA) was favorably associated with disease activity, while the omega-9 PUFA Mead acid was linked to increased pain. DISCUSSION: These findings underscore the prospect that increased tissue levels of long-chain omega-3 PUFAs, particularly DHA, are favorably associated with SLE outcomes. Although further research is needed to establish causal relationships, existing evidence supports the role of omega-3 PUFAs in managing cardiovascular and chronic kidney disease, common SLE comorbidities. Most study participants exhibited low omega-3 PUFA status, suggesting substantial potential for improvement through targeted dietary interventions and supplementation. This study supports a potential role for precision nutrition in comprehensive SLE management, considering the impact of PUFAs, SFAs and MUFAs.

Telomere length (TL) predicts the onset of replicative senescence, and its shortening is a limiter on the number of divisions individual somatic cells can perform. Metal-induced genotoxic events are discussed in Agency for Toxic Substances and Disease Registry’s (ATSDR) toxicological profiles. In vivo and in vitro toxicological studies suggest the correlation between toxic metals and TL. However, the correlation between TL and exposure to toxic metals in human populations is unclear despite decades of observational research. We conducted a literature search within the ATSDR toxicological profiles and PubMed database for peer-reviewed articles as of 04/2023 discussing TL and metal exposure in human populations. Through review of the 272 publications meeting these criteria, we identified 25 observational studies that considered the correlation between TL and exposure to some or all of six metals: cadmium (Cd), arsenic (As), nickel (Ni), selenium (Se), lead (Pb), and cesium (Cs). Because reported effect sizes were often not comparable across studies, we performed a sign test based on the reported significance for each metal–TL correlation. We found that Cd was consistently significantly correlated with shorter telomeres (p = 0.016). However, no consistent linear relationship was observed between TL and any of the other metals considered. Exploring this association can enhance our understanding of how metal exposure may influence TL dysfunction. Our findings suggest that Cd exposure contributes to shorter TL, which may affect the DNA damage response (DDR) resulting in numerous chronic health conditions. Further, we highlight inconsistencies in findings on the correlation between metal exposure and TL across different populations and exposure levels. This suggests that correlations between some metals and TL may vary across populations, and that correlations may change at different exposure levels. Also, our findings suggest the need for further research on the potential for nonlinear relationships and non-additive effects of co-exposure to multiple hazardous metals, which could explain the inconsistencies observed across studies. The inconsistent incidences of metal–TL correlations justify additional exploration into the complex interaction between metals and TL. © 2024 by the authors.

BACKGROUND: Population-based incidence data on young-adult-onset type 1 diabetes and type 2 diabetes are limited. We aimed to examine secular trends in the incidence of diagnosed type 1 diabetes and type 2 diabetes with an age of onset between 15 and 39 years. METHODS: In this multicountry aggregate data analysis, we assembled eight administrative datasets from high-income jurisdictions and countries (Australia, Denmark, Finland, Hungary, Japan, Scotland, South Korea, and Spain [Catalonia]) that had appropriate data available from an international diabetes consortium (GLOBODIAB) describing incidence by diabetes type among people aged 15-39 years from 2000 to 2020. We modelled type 1 diabetes and type 2 diabetes incidence rates using Poisson regression including age and calendar time by sex. FINDINGS: During the years 2000-20, there were 349 591 incident diabetes (both types) cases from 346 million person-years of follow-up among people aged 15-39 years. Over time, there was no statistically significant change in the incidence of type 1 diabetes in Hungary and Japan. The incidence of type 1 diabetes significantly increased in Australia, Denmark, Finland, Scotland, South Korea, and Spain, with annual changes ranging from 0·5% to 6·0%. The incidence of type 2 diabetes significantly increased in four of eight jurisdictions (Denmark, Finland, Japan, and South Korea), with annual increases from 2·0% to 8·5%. The magnitude of increase in incidence of type 2 diabetes was greater in Asian than non-Asian jurisdictions. There was no statistically significant change in type 2 diabetes incidence in Australia and Hungary. The incidence of type 2 diabetes significantly decreased in Scotland and Spain, with annual changes of -0·7% and -1·5%, respectively. INTERPRETATION: There is variability in the trajectory of the incidence of young-adult-onset type 2 diabetes among high-income countries or jurisdictions, with a greater evidence of increase in Asian than non-Asian countries. Evolving trends in the incidence of type 1 and type 2 diabetes in young adults call for the ongoing surveillance of diabetes incidence and a greater research focus on this population. FUNDING: US Centers for Disease Control and Prevention, Diabetes Australia Research Programme, and Victoria State Government Operational Infrastructure Support Programme.

Environmental exposures are ubiquitous and play a significant, and sometimes understated, role in public health as they can lead to the development of various chronic and infectious diseases. In an ideal world, there would be sufficient experimental data to determine the health effects of exposure to priority environmental contaminants. However, this is not the case, as emerging chemicals are continuously added to this list, furthering the data gaps. Recently, simulation science has evolved and can provide appropriate solutions using a multitude of computational methods and tools. In its quest to protect communities across the country from environmental health threats, ATSDR employs a variety of simulation science tools such as Physiologically Based Pharmacokinetic (PBPK) modeling, Quantitative Structure-Activity Relationship (QSAR) modeling, and benchmark dose (BMD) modeling, among others. ATSDR's use of such tools has enabled the agency to evaluate exposures in a timely, efficient, and effective manner. ATSDR's work in simulation science has also had a notable impact beyond the agency, as evidenced by external researchers' widespread appraisal and adaptation of the agency's methodology. ATSDR continues to advance simulation science tools and their applications by collaborating with researchers within and outside the agency, including other federal/state agencies, NGOs, the private sector, and academia.

CONTEXT: Public health programs promote numerous tickborne disease (TBD) prevention measures. However, measures are not frequently or consistently performed. OBJECTIVE: Describe barriers to consistent use of 4 commonly promoted TBD prevention measures. DESIGN: We conducted an online survey (n = 1883) evaluating behaviors regarding TBD prevention measures including conducting tick checks, applying insect repellents, showering/bathing, and applying chemical or natural pesticides to residential yards. Respondents could select reasons for never, rarely, or sometimes performing these measures. Descriptive analysis and logistic regression modeling evaluated associations between the 3 most cited barriers for each measure and select demographic variables. SETTING: The survey was administered to residents in high Lyme disease incidence counties of Connecticut and Maryland, 2016-2017. RESULTS: For tick checks (n = 800), the most cited barriers were forgetting (63%), not spending time in tick habitat (28%), and too much trouble (11%). For applying insect repellents (n = 1303), the most cited barriers were forgetting (38%), personal safety concerns (24%), and too much trouble (19%). For showering/bathing 2 hours after outdoor activity in tick habitat (n = 1080), the most cited barriers were being unaware of the prevention measure (51%), too much trouble (18%), and forgetting (18%). For applying chemical pesticides to yards (n = 1320), the most cited barriers were having environmental (45%), pet safety (31%), and personal safety concerns (28%). Lastly, for applying natural pesticides to yards (n = 1357), the most cited barriers were being unaware of natural pesticides (31%), having cost concerns (23%), and not being concerned about ticks on property (16%). CONCLUSIONS: Forgetting, too much trouble, unawareness, and safety concerns were primary barriers to using several TBD prevention measures. Education regarding effectiveness, safety, and timing may increase uptake of certain measures. These challenges can be difficult to address, highlighting the need for passive TBD prevention measures, such as a Lyme disease vaccine.

BACKGROUND: Scale up of proven malaria control interventions has not been sufficient to control malaria in Uganda, emphasizing the need to explore innovative new approaches. Improved housing is one such promising strategy. This paper describes housing characteristics and their association with malaria burden in a moderate to high transmission setting in Uganda. METHODS: Between October and November 2021, a household survey was conducted in 1500 randomly selected households in Jinja and Luuka districts. Information on demographics, housing characteristics, use of malaria prevention measures, and proxy indicators of wealth were collected for each household. A finger-prick blood sample was obtained for thick blood smears for malaria from all children aged 6 months to 14 years in the surveyed households. Febrile children had a malaria rapid diagnostics test (RDT) done; positive cases were managed according to national treatment guidelines. Haemoglobin was assessed in children aged < 5 years. Households were stratified as having modern houses (defined as having finished materials for roofs, walls, and floors and closed eaves) or traditional houses (those not meeting the definition of modern house). Associations between malaria burden and house type were estimated using mixed effects models and adjusted for age, wealth, and bed net use. RESULTS: Most (65.5%) of the households surveyed lived in traditional houses. Most of the houses had closed eaves (85.5%), however, the use of other protective features like window/vent screens and installed ceilings was limited (0.4% had screened windows, 2.8% had screened air vents, and 5.2% had ceiling). Overall, 3,443 children were included in the clinical survey, of which 31.4% had a positive smear. RDT test positivity rate was 56.6% among children with fever. Participants living in modern houses had a significantly lower parasite prevalence by microscopy (adjusted prevalence ratio [aPR = 0.80]; 95% confidence interval [CI] 0.71 - 0.90), RDT test positivity rate (aPR = 0.90, 95%CI 0.81 - 0.99), and anaemia (aPR = 0.80, 95%CI 0.65 - 0.97) compared to those in traditional houses. CONCLUSION: The study found that even after adjusting for wealth, higher quality housing had a moderate protective effect against malaria, on top of the protection already afforded by recently distributed nets.

The goals of the Association for Molecular Pathology (AMP) Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum set of variant alleles (Tier 1) and an extended list of variant alleles (Tier 2) that will aid clinical laboratories when designing assays for PGx testing. The AMP PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, and other technical considerations for PGx testing when developing these recommendations. The goal of this Working Group is to promote standardization of PGx testing across clinical laboratories. This document will focus on clinical DPYD PGx testing that may be applied to all DPD-related medications. These recommendations are not to be interpreted as prescriptive but to provide a reference guide.

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.

This paper describes the factors that support recovery of public health infrastructure (PHI), including conditions that facilitated or hindered recovery in United States (US) territories impacted by hurricanes Irma and Maria. A deductive approach was used to categorize data from five organizations that received crisis hurricane recovery (CHR) funds from the Centers for Disease Control and Prevention.* Spending was grouped into five infrastructure gaps: (1) human resources, (2) informatic upgrades, (3) equipment, (4) minor repairs, and (5) preventive maintenance. Unanticipated PHI costs, facilitators, and hinderances to PHI recovery were identified. Most (72 percent) of the $53,529,823 CHR funding was used to address infrastructure gaps in (1) human resources (56 percent), (2) informatics (16 percent), (3) equipment (13 percent), (4) minor repairs (10 percent), and (5) preventive maintenance (5 percent). Most of the requests (56 percent) to redirect funds were associated with unanticipated costs in initial work plans and budgets. The use of administrative partners, planning tools, dedicated staff, streamlined procedures, eg, contracts, and cost sharing facilitated PHI recovery. The most common hindrance to PHI recovery were delays in procurement and shipping. In summary, investments in dedicated funding to upgrade, repair, or replace critical structures and systems for infectious disease surveillance, laboratory capacity, vector control, environmental health inspections, and vaccine storage and administration in Puerto Rico and the US Virgin Islands after Hurricanes Irma and Maria contributed to their recovery capacity. These findings may inform funding and resource allocation considerations for PHI recovery in the US territories.

BACKGROUND: Human adenoviruses (HAdVs) can cause outbreaks of flu-like illness in university settings. Most infections in healthy young adults are mild; severe illnesses rarely occur. In Fall 2022, an adenovirus outbreak was identified in university students. METHODS: HAdV cases were defined as university students 17-26 years old who presented to the University Health Service or nearby emergency department with flu-like symptoms (eg, fever, cough, headache, myalgia, nausea) and had confirmed adenovirus infections by polymerase chain reaction (PCR). Demographic and clinical characteristics were abstracted from electronic medical records; clinical severity was categorized as mild, moderate, severe, or critical. We performed contact investigations among critical cases. A subset of specimens was sequenced to confirm the HAdV type. RESULTS: From 28 September 2022 to 30 January 2023, 90 PCR-confirmed cases were identified (51% female; mean age, 19.6 years). Most cases (88.9%) had mild illness. Seven cases required hospitalization, including 2 critical cases that required intensive care. Contact investigation identified 44 close contacts; 6 (14%) were confirmed HAdV cases and 8 (18%) reported symptoms but never sought care. All typed HAdV-positive specimens (n = 36) were type 4. CONCLUSIONS: While most students with confirmed HAdV had mild illness, 7 otherwise healthy students had severe or critical illness. Between the relatively high number of hospitalizations and proportion of close contacts with symptoms who did not seek care, the true number of HAdV cases was likely higher. Our findings illustrate the need to consider a wide range of pathogens, even when other viruses are known to be circulating.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Sickle cell disease (SCD) remains a public health priority in the United States because of its association with complex health needs, reduced life expectancy, lifelong disabilities, and high cost of care. A cross-sectional analysis was conducted to calculate the crude and race-specific birth prevalence for SCD using state newborn screening program records during 2016-2020 from 11 Sickle Cell Data Collection program states. The percentage distribution of birth mother residence within Social Vulnerability Index quartiles was derived. Among 3,305 newborns with confirmed SCD (including 57% with homozygous hemoglobin S or sickle β-null thalassemia across 11 states, 90% of whom were Black or African American [Black], and 4% of whom were Hispanic or Latino), the crude SCD birth prevalence was 4.83 per 10,000 (one in every 2,070) live births and 28.54 per 10,000 (one in every 350) non-Hispanic Black newborns. Approximately two thirds (67%) of mothers of newborns with SCD lived in counties with high or very high levels of social vulnerability; most mothers lived in counties with high or very high levels of vulnerability for racial and ethnic minority status (89%) and housing type and transportation (64%) themes. These findings can guide public health, health care systems, and community program planning and implementation that address social determinants of health for infants with SCD. Implementation of tailored interventions, including increasing access to transportation, improving housing, and advancing equity in high vulnerability areas, could facilitate care and improve health outcomes for children with SCD.

IMPORTANCE: Caring for children diagnosed with cancer may adversely affect the mental health (MH) of parents. OBJECTIVE: To characterize utilization of MH services among parents of children with vs without cancer using nationwide commercial claims data. DESIGN, SETTING, AND PARTICIPANTS: For this cross-sectional study, the Merative MarketScan Commercial Claims Database was used to identify continuously insured families of children treated for cancer (aged ≤21 years at diagnosis) during 2010 to 2018, compared with families who matched eligibility criteria but did not have a child with a cancer history. Parents were assessed from 18 months before to 12 months after their child's cancer diagnosis. Analyses were conducted from February 2022 to September 2023. EXPOSURES: Children's cancer diagnosis. MAIN OUTCOMES AND MEASURES: Outcomes included parents' MH-related visits during the first year following their child's cancer diagnosis. Logistic regressions compared outcomes between families of children with vs without cancer, adjusting for sociodemographic and clinical factors. RESULTS: This study included 4837 families of children with cancer (4210 mothers and 4016 fathers) and 24 185 families of children without cancer (21 444 mothers and 19 591 fathers) with continuous insurance enrollment. Most household leads were aged 35 to 54 years (3700 [76.5%] in families of children with cancer vs 17 812 [73.6%] in families of children without cancer) and resided in urban areas (4252 [87.9%] vs 21 156 [87.5%]). The probabilities of parents having anxiety-related visits (10.6% vs 7.0%), depression-related visits (8.4% vs 6.1%), and any MH-related visits (18.1% vs 13.3%) were higher in families of children with vs without cancer. Adjusted analyses showed absolute increases of 3.2 percentage points (95% CI, 2.3 to 4.0; 45.7% relative increase), 2.2 percentage points (95% CI, 1.4 to 3.0; 36.1% relative increase), and 4.2 percentage points (95% CI, 3.1 to 5.3; 31.3% relative increase) in the probabilities of 1 or both parents having anxiety-related visits, depression-related visits, and any MH-related visits, respectively, among families of children with vs without cancer. Such differences were greater in magnitude among mothers than fathers. CONCLUSIONS AND RELEVANCE: In this cohort study of privately insured parents, those caring for children with cancer had a higher likelihood of utilizing MH care than other parents. These findings underline the importance of interventions toward targeted counseling and support to better meet MH care needs among parents and caregivers of children with cancer.

Many SARS-CoV-2 infections are asymptomatic, thus reported cases underestimate actual cases. To improve estimates, we conducted surveillance for SARS-CoV-2 seroprevalence among pregnant women attending their first antenatal care visit (ANC1) from June 2021 through May 2022. We administered a questionnaire to collect demographic, risk factors, and COVID-19 vaccine status information and tested dried blood spots for SARS-CoV-2 antibodies. Although <1% of ANC1 participants reported having had COVID-19, monthly SARS-CoV-2 seroprevalence increased from 15.4% (95% CI: 10.5-21.5) in June 2021 to 65.5% (95% CI: 55.5-73.7) in May 2022. Although COVID-19 vaccination was available in March 2021, uptake remained low, reaching a maximum of 9.5% (95% CI: 5.7-14.8) in May 2022. Results of ANC1 serosurveillance provided prevalence estimates helpful in understanding this population case burden that was available through self-report and national case reports. To improve vaccine uptake, efforts to address fears and misconceptions regarding COVID-19 vaccines are needed.

Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality. Monitoring VTE cases is limited by the challenges of manual chart review and diagnosis code interpretation. Natural language processing (NLP) can automate the process. Rule-based NLP methods are effective but time consuming. Machine learning (ML)-NLP methods present a promising solution. We conducted a systematic review and meta-analysis of studies published before May 2023 that use ML-NLP to identify VTE diagnoses in the electronic health records. Four reviewers screened all manuscripts, excluding studies that only used a rule-based method. A meta-analysis evaluated the pooled performance of each study's best performing model that evaluated for pulmonary embolism (PE) and/or deep vein thrombosis (DVT). Pooled sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with confidence interval (CI) were calculated by DerSimonian and Laird method using a random-effects model. Study quality was assessed using an adapted TRIPOD tool. Thirteen studies were included in the systematic review and 8 had data available for meta-analysis. Pooled sensitivity was 0.931 (95% CI 0.881-0.962), specificity 0.984 (95% CI 0.967-0.992), PPV 0.910 (95% CI 0.865-0.941) and NPV 0.985 (95% CI 0.977-0.990). All studies met at least 13 of the 21 NLP-modified TRIPOD items, demonstrating fair quality. The highest performing models used vectorization rather than bag-of-words, and deep learning techniques such as convolutional neural networks. There was significant heterogeneity in the studies and only four validated their model on an external dataset. Further standardization of ML studies can help progress this novel technology towards real-world implementation.

BACKGROUND: Insecticide-treated nets (ITNs) contributed significantly to the decline in malaria since 2000. Their protective efficacy depends not only on access, use, and net integrity, but also location of people within the home environment and mosquito biting profiles. Anopheline mosquito biting and human location data were integrated to identify potential gaps in protection and better understand malaria transmission dynamics in Busia County, western Kenya. METHODS: Direct observation of human activities and human landing catches (HLC) were performed hourly between 1700 to 0700 h. Household members were recorded as home or away; and, if at home, as indoors/outdoors, awake/asleep, and under a net or not. Aggregated data was analysed by weighting hourly anopheline biting activity with human location. Standard indicators of human-vector interaction were calculated using a Microsoft Excel template. RESULTS: There was no significant difference between indoor and outdoor biting for Anopheles gambiae sensu lato (s.l.) (RR = 0.82; 95% CI 0.65-1.03); significantly fewer Anopheles funestus were captured outdoors than indoors (RR = 0.41; 95% CI 0.25-0.66). Biting peaked before dawn and extended into early morning hours when people began to awake and perform routine activities, between 0400-0700 h for An. gambiae and 0300-0700 h for An. funestus. The study population away from home peaked at 1700-1800 h (58%), gradually decreased and remained constant at 10% throughout the night, before rising again to 40% by 0600-0700 h. When accounting for resident location, nearly all bites within the peri-domestic space (defined as inside household structures and surrounding outdoor spaces) occurred indoors for unprotected people (98%). Using an ITN while sleeping was estimated to prevent 79% and 82% of bites for An. gambiae and An. funestus, respectively. For an ITN user, most remaining exposure to bites occurred indoors in the hours before bed and early morning. CONCLUSION: While use of an ITN was estimated to prevent most vector bites in this context, results suggest gaps in protection, particularly in the early hours of the morning when biting peaks and many people are awake and active. Assessment of additional human exposure points, including outside of the peri-domestic setting, are needed to guide supplementary interventions for transmission reduction.

Since 2008, Mainland China has undergone widespread outbreaks of hand, foot, and mouth disease (HFMD). In order to determine the characteristics of epidemics and enteroviruses (EV) associated with HFMD in Tianjin, in northern China, epidemiological and virological data from routine surveillance were collected and analyzed. In Tianjin, a persistent epidemic of HFMD was demonstrated during 2008-2013, involving 102,705 mild, 179 severe, and 16 fatal cases. Overall, 8234 specimens were collected from 7829 HFMD patients for EV detection during 2008-2013. Enterovirus 71 (EV-A71) and coxsackievirus A16 (CV-A16) were the dominant serotypes during 2008-2012, and they were replaced by CV-A6 as the major causative agent in 2013. Phylogenetic analysis based on complete VP1 nucleotide sequences revealed that multiple CV-A6 lineages co-circulated in Tianjin, which grouped together with strains from China and other countries and split into two distinct clusters (clusters 1 and 2). Most Tianjin strains grouped in cluster 1 and were closely related to strains from several eastern and southern provinces of China during 2012 and 2013. Estimates from Bayesian MCMC analysis suggested that multiple lineages had been transmitted silently before the outbreaks at an estimated evolutionary rate of 4.10 × 10(-3) substitutions per site per year without a specific distribution of rate variances among lineages. The sudden outbreak of CV-A6 in Tianjin during 2013 is attributed to indigenous CV-A6 lineages, which were linked to the wide spread of endemic strains around eastern and southern China.

PURPOSE: Database linkage between cancer registries and clinical trial consortia has the potential to elucidate referral patterns of children and adolescents with newly diagnosed cancer, including enrollment into cancer clinical trials. This study's primary objective was to assess the feasibility of this linkage approach. METHODS: Patients younger than 20 years diagnosed with incident cancer during 2012-2017 in the Kentucky Cancer Registry (KCR) were linked with patients enrolled in a Children's Oncology Group (COG) study. Matched patients between databases were described by sex, age, race and ethnicity, geographical location when diagnosed, and cancer type. Logistic regression modeling identified factors associated with COG study enrollment. Timeliness of patient identification by KCR was reported through the Centers for Disease Control and Prevention's Early Case Capture (ECC) program. RESULTS: Of 1,357 patients reported to KCR, 47% were determined by matching to be enrolled in a COG study. Patients had greater odds of enrollment if they were age 0-4 years (v 15-19 years), reported from a COG-affiliated institution, and had renal cancer, neuroblastoma, or leukemia. Patients had lower odds of enrollment if Hispanic (v non-Hispanic White) or had epithelial (eg, thyroid, melanoma) cancer. Most (59%) patients were reported to KCR within 10 days of pathologic diagnosis. CONCLUSION: Linkage of clinical trial data with cancer registries is a feasible approach for tracking patient referral and clinical trial enrollment patterns. Adolescents had lower enrollment compared with younger age groups, independent of cancer type. Population-based early case capture could guide interventions designed to increase cancer clinical trial enrollment.

Hospital-associated venous thromboembolism (VTE) is a major public health challenge, and while thromboprophylaxis is known to be effective, it remains misused [1]. Clinicians face enormous complexity when determining who should receive thromboprophylaxis. To better understand current practices around VTE prophylaxis in adult hospitalized patients, we previously surveyed 607 clinicians across the United States between 2021 and 2022 [2]. Overall, 48% of respondents reported patients at their institution are not on appropriate VTE prophylaxis almost all the time. The majority reported that technology such as artificial intelligence (AI) may help improve rates of appropriate prophylaxis. However, only 35% reported using existing risk assessment models (RAMs); 68% reported using their own clinical assessment instead. Therefore, we invited survey respondents to participate in focus groups to better understand how they approach VTE prophylaxis, with a focus on their perspectives regarding using AI decision support.

Malaria remains a leading cause of childhood morbidity and mortality in sub-Saharan Africa, particularly among children under 5 years of age. To help address this challenge, the WHO recommends chemoprevention for certain populations. For children and infants, the WHO recommends seasonal malaria chemoprevention (SMC), perennial malaria chemoprevention (PMC; formerly intermittent preventive treatment in infants [IPTi]), and, more recently, intermittent preventive treatment in school children (IPTsc). This review describes the contextual factors, including feasibility, acceptability, health equity, financial considerations, and values and preferences, that impact implementation of these strategies. A systematic search was conducted on July 5, 2022, and repeated April 13, 2023, to identify relevant literature. Two reviewers independently screened titles for eligibility, extracted data from eligible articles, and identified and summarized themes. Of 6,295 unique titles identified, 65 were included. The most frequently evaluated strategy was SMC (n = 40), followed by IPTi (n = 18) and then IPTsc (n = 6). Overall, these strategies were highly acceptable, although with IPTsc, there were community concerns with providing drugs to girls of reproductive age and the use of nonmedical staff for drug distribution. For SMC, door-to-door delivery resulted in higher coverage, improved caregiver acceptance, and reduced cost. Lower adherence was noted when caregivers were charged with giving doses 2 and 3 unsupervised. For SMC and IPTi, travel distances and inclement weather limited accessibility. Sensitization and caregiver education efforts, retention of high-quality drug distributors, and improved transportation were key to improving coverage. Additional research is needed to understand the role of community values and preferences in chemoprevention implementation.

After the 2015 documentation of global eradication of wild poliovirus type 2,* Sabin type 2 oral poliovirus vaccine (OPV) was withdrawn from routine immunization (RI) in all OPV-using countries in 2016, in a global synchronized switch from trivalent OPV (containing vaccine virus serotypes 1, 2, and 3) to bivalent OPV (containing serotypes 1 and 3), to reduce the rare risks for type 2 vaccine-associated paralytic poliomyelitis. Concurrently, the Global Polio Eradication Initiative (GPEI) recommended that all OPV-using countries introduce ≥1 dose of inactivated poliovirus vaccine (IPV) into RI programs; IPV protects against paralysis caused by all three serotypes but cannot be transmitted from person to person or cause paralysis. Use of OPV, especially in areas with low vaccination coverage, is associated with low risk of emergence of vaccine-derived polioviruses (VDPVs). As susceptible persons in new birth cohorts accumulated after withdrawal of OPV type 2, population immunity against infection with serotype 2 declined (1), facilitating the emergence of circulating VDPV type 2 (cVDPV2). During the previous 7 years, cVDPV2 outbreaks required response supplementary immunization activities (SIAs) with monovalent type 2 OPV (mOPV2); however, if SIAs were not of sufficiently high quality and did not achieve high enough coverage, new emergences of cVDPV2 occurred.

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

BACKGROUND: Accurate diagnostic and prognostic predictions of venous thromboembolism (VTE) are crucial for VTE management. Artificial intelligence (AI) enables autonomous identification of the most predictive patterns from large complex data. Although evidence regarding its performance in VTE prediction is emerging, a comprehensive analysis of performance is lacking. AIMS: To systematically review the performance of AI in the diagnosis and prediction of VTE and compare it to clinical risk assessment models (RAMs) or logistic regression models. METHODS: A systematic literature search was performed using PubMed, MEDLINE, EMBASE, and Web of Science from inception to April 20, 2021. Search terms included "artificial intelligence" and "venous thromboembolism." Eligible criteria were original studies evaluating AI in the prediction of VTE in adults and reporting one of the following outcomes: sensitivity, specificity, positive predictive value, negative predictive value, or area under receiver operating curve (AUC). Risks of bias were assessed using the PROBAST tool. Unpaired t-test was performed to compare the mean AUC from AI versus conventional methods (RAMs or logistic regression models). RESULTS: A total of 20 studies were included. Number of participants ranged from 31 to 111 888. The AI-based models included artificial neural network (six studies), support vector machines (four studies), Bayesian methods (one study), super learner ensemble (one study), genetic programming (one study), unspecified machine learning models (two studies), and multiple machine learning models (five studies). Twelve studies (60%) had both training and testing cohorts. Among 14 studies (70%) where AUCs were reported, the mean AUC for AI versus conventional methods were 0.79 (95% CI: 0.74-0.85) versus 0.61 (95% CI: 0.54-0.68), respectively (p < .001). However, the good to excellent discriminative performance of AI methods is unlikely to be replicated when used in clinical practice, because most studies had high risk of bias due to missing data handling and outcome determination. CONCLUSION: The use of AI appears to improve the accuracy of diagnostic and prognostic prediction of VTE over conventional risk models; however, there was a high risk of bias observed across studies. Future studies should focus on transparent reporting, external validation, and clinical application of these models.