Association of folate concentrations with clinical signs and laboratory markers of chronic enteropathy in dogs.

BACKGROUND: Serum folate is considered a biomarker of chronic enteropathy (CE) in dogs, but few studies have examined associations with markers of CE. HYPOTHESIS/OBJECTIVES: To evaluate serum folate concentrations in dogs with and without CE and associations with sample hemolysis and selected markers of CE. We hypothesized that hypofolatemia would be more common in dogs with CE and associated with hypocobalaminemia, higher CIBDAI, and hypoalbuminemia. ANIMALS: Six hundred seventy-three dogs with available serum folate measurements performed at an academic veterinary hospital between January 2016 and December 2019. METHODS: Medical records were retrospectively reviewed to categorize cases as CE or non-CE and record clinical details and laboratory markers. Relationships between serum folate, cobalamin, and CE variables were assessed using chi-square, Kruskal-Wallis, or Spearman's correlation tests. RESULTS: Of the 673 dogs, 99 CE were compared to 95 non-CE. In the overall cohort, serum folate concentration did not correlate with sample hemolysis (P = .75). In the CE subset, serum folate and cobalamin concentrations were positively associated (rho = 0.34, FDR = 0.02). However, serum folate concentrations (median [25th, 75th percentiles]) were higher (CE: 12.1 (8.9, 16.1), non-CE: 10.4 (7.2, 15.5); P = .04) and cobalamin concentrations were lower (CE: 343 (240, 597), non-CE: 550 (329, 749); P = .001) in the CE vs non-CE group. Serum folate was not associated with markers of CE, but serum cobalamin was associated with albumin (P = .04) and cholesterol (P = .03). CONCLUSIONS AND CLINICAL IMPORTANCE: Hypofolatemia is an inferior biomarker of CE compared to hypocobalaminemia.

An examination of the association between altitude and suicide deaths, suicide attempts, and suicidal ideation among veterans at both the patient and geospatial level.

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation.

A time-varying vulnerability index for COVID-19 in New Mexico, USA using generalized propensity scores.

The coronavirus disease (COVID-19) pandemic has highlighted systemic inequities in the United States and resulted in a larger burden of negative social outcomes for marginalized communities. New Mexico, a state in the southwestern US, has a unique population with a large racial minority population and a high rate of poverty that may make communities more vulnerable to negative social outcomes from COVID-19. To identify which communities may be at the highest relative risk, we created a county-level vulnerability index. After the first COVID-19 case was reported in New Mexico on March 11, 2020, we fit a generalized propensity score model that incorporates sociodemographic factors to predict county-level viral exposure and thus, the generic risk to negative social outcomes such as unemployment or mental health impacts. We used four static sociodemographic covariates important for the state of New Mexico-population, poverty, household size, and minority population-and weekly cumulative case counts to iteratively run our model each week and normalize the exposure score to create a time-varying vulnerability index. We found the relative vulnerability between counties varied in the first eight weeks from the initial COVID-19 case before stabilizing. This framework for creating a location-specific vulnerability index in response to an ongoing disaster may be used as a quick, deployable metric to inform health policy decisions such as allocating state resources to the county level.

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Health Care Needs of the State: Quantitative Analysis.

BACKGROUND: Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. OBJECTIVE: Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. METHODS: We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory's COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. RESULTS: Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. CONCLUSIONS: Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study.

BACKGROUND: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. OBJECTIVE: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. METHODS: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. RESULTS: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to -0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. CONCLUSIONS: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.

BACKGROUND: The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts. OBJECTIVE: The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic. METHODS: We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time. RESULTS: Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events. CONCLUSIONS: Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.

Differences in Incidence and Case Fatality of Abusive Head Trauma.

BACKGROUND: Abusive head trauma (AHT) in children older than 1 and younger than 5 years old is thought uncommon and rarely studied. OBJECTIVE: This study estimates national incidence and case fatality rate of abusive head trauma (AHT), and evaluates differences by age, sex, race, and region, with a focus on children of 2-4 years. PARTICIPANTS AND SETTING: Hospital discharges were extracted from The Healthcare Cost and Utilization Project's Kids' Inpatient Database from 2000, 2003, 2006, 2009, and 2012 using the CDC's narrow definition of AHT. METHODS: Survey-weighted chi-square tests were used to assess differences in incidence and case fatality rates. RESULTS: The average annual incidence per 100,000 children was highest in <1 year-olds (27), followed by age 1 (4), age 2 (3), and age 3-4 (1). Average annual incidence varied significantly by sex (p = 0.0001), race (p < 0.0001), and region (p = 0.0002) within each age category. The average annual case fatality rate increased significantly with age, with a rate of 0.10 among children age <1 year, 0.15 for age 1, 0.23 for age 2, and 0.20 for age 3-4 years. The average annual case fatality rate was higher in the South (0.12) than West (0.10), Midwest (0.09), and Northeast (0.08) among children <1 year of age. CONCLUSIONS: Black and Hispanic children and hospitals in the Midwest experienced higher incidence of AHT than White children and Northeast hospitals, respectively, especially in cases <1 year of age. Case fatality rates increased significantly with age, and the South experienced the highest rates for infants <1 year.