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This brief methods paper is being published concomitantly with “Preliminary Incidence and Trends of Infections Caused by Pathogens Transmitted Commonly Through Food— Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2016–2021” in Morbidity and Mortality Weekly Reports (MMWR). That article describes the application of the new model described here to analyze trends and evaluate progress towards the prevention of infection from enteric pathogens in the United States.

Background We quantified the risk of respiratory syncytial virus (RSV) hospitalizations and severe outcomes among children with neurological disorders. Methods We estimated RSV-specific and RSV-associated hospitalization rates using International Classification of Diseases, Ninth Revision (ICD-9) codes from 2 insurance claims IBM MarketScan Research Databases (Commercial and Multi-State Medicaid) from July 2006 through June 2015. For comparison, a simple random sample of 10% of all eligible children was selected to represent the general population. Relative rates (RRs) of RSV hospitalization were calculated by dividing rates for children with neurological disorders by rates for children in the general population by age group and season. Results The RSV-specific hospitalization rate for children with any neurological condition was 4.2 (95% confidence interval [CI]: 4.1, 4.4) per 1000 person-years, and the RSV-associated hospitalization rate was 7.0 (95% CI: 6.9, 7.2) per 1000 person-years among children Conclusions Our population-based study of children with neurological disorders found that the risk of RSV hospitalization was 6 to 12 times higher among children with neurological disorders than among the general pediatric population. These findings should be considered when determining who should be targeted for current and future RSV interventions.

Purpose Youth suicide clusters may be exacerbated by suicide contagion—the spread of suicidal behaviors. Factors promoting suicide contagion are poorly understood, particularly in the advent of social media. Using cross-sectional data from an ongoing youth suicide cluster in Ohio, this study examines associations between suicide cluster-related social media and suicidal behaviors. Methods We surveyed 7th- to 12th-grade students in northeastern Ohio during a 2017–2018 suicide cluster to assess the prevalence of suicidal ideation (SI), suicide attempts (SAs), and associations with potential contagion-promoting factors such as suicide cluster–related social media, vigils, memorials, news articles, and watching the Netflix series 13 Reasons Why before or during the cluster. Generalized estimating equations examined associations between potential contagion-promoting factors and SI/SA, adjusting for nonmodifiable risk factors. Subgroup analyses examined whether associations between cluster-related factors and SI/SA during the cluster varied by previous history of SI/SA. Results Among participating students, 9.0% (876/9,733) reported SI and 4.9% attempted suicide (481/9,733) during the suicide cluster. Among students who posted suicide cluster–related content to social media, 22.9% (267/1,167) reported SI and 15.0% (175/1,167) attempted suicide during the suicide cluster. Posting suicide cluster–related content was associated with both SI (adjusted odds ratio 1.7, 95% confidence interval 1.4–2.0) and SA during the cluster (adjusted odds ratio 1.7, 95% confidence interval 1.2–2.5). In subgroup analyses, seeing suicide cluster–related posts was uniquely associated with increased odds of SI and SA during the cluster among students with no previous history of SI/SA. Conclusions Exposure to suicide cluster–related social media is associated with both SI and SA during a suicide cluster. Suicide interventions could benefit from efforts to mitigate potential negative effects of social media and promote prevention messages.

Background Outbreaks of respiratory syncytial virus (RSV) in neonatal intensive care units (NICUs) are of concern because of the risk of severe disease in young infants. We describe an outbreak of RSV in a NICU and use whole genome sequencing (WGS) to better understand the relatedness of viruses among patients. Methods An investigation was conducted to identify patients and describe their clinical course. Infection control measures were implemented to prevent further spread. Respiratory specimens from outbreak-related patients and the community were tested using WGS. Phylogenetic trees were constructed to understand relatedness of the viruses. Results Seven patients developed respiratory symptoms within an 11-day span in December 2017 and were diagnosed with RSV; 6 patients (86%) were preterm and 1 had chronic lung disease. Three patients required additional respiratory support after symptom onset, and none died. Six of 7 patients were part of the same cluster based on > 99.99% nucleotide agreement with each other and 3 unique single-nucleotide polymorphisms were identified in viruses sequenced from those patients. The seventh patient was admitted from the community with respiratory symptoms and had a genetically distinct virus that was not related to the other 6. Implementation of enhanced infection control measures likely limited the spread. Conclusions Using WGS, we found 2 distinct introductions of RSV into a NICU, highlighting the risk of healthcare-associated infections during RSV season. Early recognition and infection control measures likely limited spread, emphasizing the importance of considering RSV in the differential diagnosis of respiratory infections in healthcare settings.

IMPORTANCE: Refinement of criteria for multisystem inflammatory syndrome in children (MIS-C) may inform efforts to improve health outcomes. OBJECTIVE: To compare clinical characteristics and outcomes of children and adolescents with MIS-C vs those with severe coronavirus disease 2019 (COVID-19). SETTING, DESIGN, AND PARTICIPANTS: Case series of 1116 patients aged younger than 21 years hospitalized between March 15 and October 31, 2020, at 66 US hospitals in 31 states. Final date of follow-up was January 5, 2021. Patients with MIS-C had fever, inflammation, multisystem involvement, and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcriptase–polymerase chain reaction (RT-PCR) or antibody test results or recent exposure with no alternate diagnosis. Patients with COVID-19 had positive RT-PCR test results and severe organ system involvement. EXPOSURE: SARS-CoV-2. MAIN OUTCOMES AND MEASURES: Presenting symptoms, organ system complications, laboratory biomarkers, interventions, and clinical outcomes. Multivariable regression was used to compute adjusted risk ratios (aRRs) of factors associated with MIS-C vs COVID-19. RESULTS: Of 1116 patients (median age, 9.7 years; 45% female), 539 (48%) were diagnosed with MIS-C and 577 (52%) with COVID-19. Compared with patients with COVID-19, patients with MIS-C were more likely to be 6 to 12 years old (40.8% vs 19.4%; absolute risk difference [RD], 21.4% [95% CI, 16.1%-26.7%]; aRR, 1.51 [95% CI, 1.33-1.72] vs 0-5 years) and non-Hispanic Black (32.3% vs 21.5%; RD, 10.8% [95% CI, 5.6%-16.0%]; aRR, 1.43 [95% CI, 1.17-1.76] vs White). Compared with patients with COVID-19, patients with MIS-C were more likely to have cardiorespiratory involvement (56.0% vs 8.8%; RD, 47.2% [95% CI, 42.4%-52.0%]; aRR, 2.99 [95% CI, 2.55-3.50] vs respiratory involvement), cardiovascular without respiratory involvement (10.6% vs 2.9%; RD, 7.7% [95% CI, 4.7%-10.6%]; aRR, 2.49 [95% CI, 2.05-3.02] vs respiratory involvement), and mucocutaneous without cardiorespiratory involvement (7.1% vs 2.3%; RD, 4.8% [95% CI, 2.3%-7.3%]; aRR, 2.29 [95% CI, 1.84-2.85] vs respiratory involvement). Patients with MIS-C had higher neutrophil to lymphocyte ratio (median, 6.4 vs 2.7, P

Prolonged symptom duration and disability are common in adults hospitalized with severe coronavirus disease 2019 (COVID-19). Characterizing return to baseline health among outpatients with milder COVID-19 illness is important for understanding the full spectrum of COVID-19-associated illness and tailoring public health messaging, interventions, and policy. During April 15-June 25, 2020, telephone interviews were conducted with a random sample of adults aged ≥18 years who had a first positive reverse transcription-polymerase chain reaction (RT-PCR) test for SARS-CoV-2, the virus that causes COVID-19, at an outpatient visit at one of 14 U.S. academic health care systems in 13 states. Interviews were conducted 14-21 days after the test date. Respondents were asked about demographic characteristics, baseline chronic medical conditions, symptoms present at the time of testing, whether those symptoms had resolved by the interview date, and whether they had returned to their usual state of health at the time of interview. Among 292 respondents, 94% (274) reported experiencing one or more symptoms at the time of testing; 35% of these symptomatic respondents reported not having returned to their usual state of health by the date of the interview (median = 16 days from testing date), including 26% among those aged 18-34 years, 32% among those aged 35-49 years, and 47% among those aged ≥50 years. Among respondents reporting cough, fatigue, or shortness of breath at the time of testing, 43%, 35%, and 29%, respectively, continued to experience these symptoms at the time of the interview. These findings indicate that COVID-19 can result in prolonged illness even among persons with milder outpatient illness, including young adults. Effective public health messaging targeting these groups is warranted. Preventative measures, including social distancing, frequent handwashing, and the consistent and correct use of face coverings in public, should be strongly encouraged to slow the spread of SARS-CoV-2.

Vector-borne diseases commonly emerge in urban landscapes, and Gaussian field models can be used to create risk maps of vector presence across a large environment. However, these models do not account for the possibility that streets function as permeable barriers for insect vectors. We describe a methodology to transform spatial point data to incorporate permeable barriers, by distorting the map to widen streets, with one additional parameter. We use Gaussian field models to estimate this additional parameter, and develop risk maps incorporating streets as permeable barriers. We demonstrate our method on simulated datasets and apply it to data on Triatoma infestans, a vector of Chagas disease in Arequipa, Peru. We found that the transformed landscape that best fit the observed pattern of Triatoma infestans infestation, approximately doubled the true Euclidean distance between neighboring houses on different city blocks. Our findings may better guide control of re-emergent insect populations.

We characterized exposures and demographics of Middle East respiratory syndrome coronavirus cases reported to the Saudi Arabia Ministry of Health during July 1–October 31, 2017, and June 1–September 16, 2018. Molecular characterization of available specimens showed that circulating viruses during these periods continued to cluster within lineage 5.

Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infection in young children worldwide (1-3). In the United States, RSV infection results in >57,000 hospitalizations and 2 million outpatient visits each year among children aged

Objectives Containing domestic vector infestation requires the ability to swiftly locate and treat infested homes. In urban settings where vectors are heterogeneously distributed throughout a dense housing matrix, the task of locating infestations can be challenging. Here, we present a novel stochastic compartmental model developed to help locate infested homes in urban areas. We designed the model using infestation data for the Chagas disease vector species Triatoma infestans in Arequipa, Peru. Methods Our approach incorporates disease vector counts at each observed house, and the vector’s complex spatial dispersal dynamics. We used a Bayesian method to augment the observed data, estimate the insect population growth and dispersal parameters, and determine posterior infestation probabilities of households. We investigated the properties of the model through simulation studies, followed by field testing in Arequipa. Results Simulation studies showed the model to be accurate in its estimates of two parameters of interest: the growth rate of a domestic triatomine bug colony and the probability of a triatomine bug successfully invading a new home after dispersing from an infested home. When testing the model in the field, data collection using model estimates was hindered by low household participation rates, which severely limited the algorithm and in turn, the model’s predictive power. Conclusions While future optimization efforts must improve the model’s capabilities when household participation is low, our approach is nonetheless an important step toward integrating data with predictive modeling to carry out evidence-based vector surveillance in cities.

We describe an outbreak of severe respiratory illness associated with human coronavirus NL63 in a long-term care facility in Louisiana in November 2017. Six of 20 case-patients were hospitalized with pneumonia, and 3 of 20 died. Clinicians should consider human coronavirus NL63 for patients in similar settings with respiratory disease.

Objectives To estimate the proportion of opioid misuse attributable to adverse childhood experiences (ACEs) among adolescents. Study design A cross-sectional survey was administered to 10 546 seventh-to twelfth-grade students in northeastern Ohio in Spring 2018. Study measures included self-reported lifetime exposure to 10 ACEs and past 30-day use of nonmedical prescription opioid or heroin. Using generalized estimating equations, we evaluated associations between recent opioid misuse, individual ACEs, and cumulative number of ACEs. We calculated population attributable fractions to determine the proportion of adolescents' recent opioid misuse attributable to ACEs. Results Nearly 1 in 50 adolescents reported opioid misuse within 30 days (1.9%); approximately 60% of youth experienced ≥1 ACE; 10.2% experienced ≥5 ACEs. Cumulative ACE exposure demonstrated a significant graded relationship with opioid misuse. Compared with youth with zero ACEs, youth with 1 ACE (aOR 1.9, 95% CI, 0.9-3.9), 2 ACEs (aOR, 3.8; 95% CI, 1.9-7.9), 3 ACEs (aOR, 3.7; 95% CI, 2.2-6.5), 4 ACEs (aOR, 5.8; 95% CI, 3.1-11.2), and ≥5 ACEs (aOR, 15.3; 95% CI, 8.8-26.6) had higher odds of recent opioid misuse. The population attributable fraction of recent opioid misuse associated with experiencing ≥1 ACE was 71.6% (95% CI, 59.8-83.5). Conclusions There was a significant graded relationship between number of ACEs and recent opioid misuse among adolescents. More than 70% of recent adolescent opioid misuse in our study population was attributable to ACEs. Efforts to decrease opioid misuse could include programmatic, policy, and clinical practice interventions to prevent and mitigate the negative effects of ACEs.

Background Until recently, the Chagas disease vector, Triatoma infestans, was widespread in Arequipa, Perú, but as a result of a decades-long campaign in which over 70,000 houses were treated with insecticides, infestation prevalence is now greatly reduced. To monitor for T. infestans resurgence, the city is currently in a surveillance phase in which a sample of houses is selected for inspection each year. Despite extensive data from the control campaign that could be used to inform surveillance, the selection of houses to inspect is often carried out haphazardly or by convenience. Therefore, we asked, how can we enhance efforts toward preventing T. infestans resurgence by creating the opportunity for vector surveillance to be informed by data? Methodology/principal findings To this end, we developed a mobile app that provides vector infestation risk maps generated with data from the control campaign run in a predictive model. The app is intended to enhance vector surveillance activities by giving inspectors the opportunity to incorporate the infestation risk information into their surveillance activities, but it does not dictate which houses to surveil. Therefore, a critical question becomes, will inspectors use the risk information? To answer this question, we ran a pilot study in which we compared surveillance using the app to the current practice (paper maps). We hypothesized that inspectors would use the risk information provided by the app, as measured by the frequency of higher risk houses visited, and qualitative analyses of inspector movement patterns in the field. We also compared the efficiency of both mediums to identify factors that might discourage risk information use. Over the course of ten days (five with each medium), 1,081 houses were visited using the paper maps, of which 366 (34%) were inspected, while 1,038 houses were visited using the app, with 401 (39%) inspected. Five out of eight inspectors (62.5%) visited more higher risk houses when using the app (Fisher’s exact test, p < 0.001). Among all inspectors, there was an upward shift in proportional visits to higher risk houses when using the app (Mantel-Haenszel test, common odds ratio (OR) = 2.42, 95% CI 2.00–2.92), and in a second analysis using generalized linear mixed models, app use increased the odds of visiting a higher risk house 2.73-fold (95% CI 2.24–3.32), suggesting that the risk information provided by the app was used by most inspectors. Qualitative analyses of inspector movement revealed indications of risk information use in seven out of eight (87.5%) inspectors. There was no difference between the app and paper maps in the number of houses visited (paired t-test, p = 0.67) or inspected (p = 0.17), suggesting that app use did not reduce surveillance efficiency. Conclusions/significance Without staying vigilant to remaining and re-emerging vector foci following a vector control campaign, disease transmission eventually returns and progress achieved is reversed. Our results suggest that, when provided the opportunity, most inspectors will use risk information to direct their surveillance activities, at least over the short term. The study is an initial, but key, step toward evidence-based vector surveillance., Author summary Chagas disease is a serious infection that is spread by blood-sucking insects called ‘kissing bugs.’ These bugs live in and around human homes, and until recently, they infested thousands of human homes throughout Arequipa, the second largest city in Perú. However, a decades-long control campaign drastically reduced the number of infested houses, and the city is now in a stage where health personnel annually inspect a sample of houses throughout the city for kissing bug reinfestation. A large amount of information was collected during the control campaign that could be used to help identify the houses at highest risk for re-infestation, so we developed a cell phone app to provide this information to health personnel in the form of interactive, user-friendly risk maps. We carried out a pilot study to see if health personnel would use these maps to select houses to inspect for re-infestation, and we found that most inspectors did use the information. We also observed that using the app did not slow the inspectors down, which can be an issue when introducing new technology. Our results suggest that the app could be a useful tool for monitoring diseases spread by insects in cities.