Feasibility of a twitter campaign to promote HPV vaccine uptake among racially/ethnically diverse young adult women living in public housing.

BACKGROUND: Uptake and completion of the HPV vaccine is suboptimal. This study assessed the feasibility of implementing a one-month Twitter campaign to promote knowledge about the human papillomavirus (HPV) vaccine among low-income women living in public housing. METHODS: We recruited a convenience sample (n = 35) of women ages 18-26 years residing in low-come, public housing in Massachusetts. We assessed the feasibility and acceptability of a communication campaign that consisted of daily Twitter messages. Online surveys assessed changes in HPV knowledge, attitudes, and vaccine intentions before and after the campaign. RESULTS: Most believed that Twitter was an acceptable educational strategy and remained engaged with the campaign throughout the intervention. We observed no changes in HPV knowledge, perceived benefits of or barriers to vaccination, decision self-efficacy, or vaccine intentions after the campaign, although perceived risk for cervical cancer decreased. CONCLUSIONS: Twitter may be a feasible and acceptable method for promoting knowledge about the HPV vaccine, but more research is needed to understand how best to reach low-income women with low levels of vaccine uptake. TRIAL REGISTRATION: Clinicaltrials.gov 1,603,045, retrospectively registered 0610/19.

Incorporating calendar effects to predict influenza seasonality in Milwaukee, Wisconsin.

Social outings can trigger influenza transmission, especially in children and elderly. In contrast, school closures are associated with reduced influenza incidence in school-aged children. While influenza surveillance modelling studies typically account for holidays and mass gatherings, age-specific effects of school breaks, sporting events and commonly celebrated observances are not fully explored. We examined the impact of school holidays, social events and religious observances for six age groups (all ages, ⩽4, 5-24, 25-44, 45-64, ⩾65 years) on four influenza outcomes (tests, positives, influenza A and influenza B) as reported by the City of Milwaukee Health Department Laboratory, Milwaukee, Wisconsin from 2004 to 2009. We characterised holiday effects by analysing average weekly counts in negative binomial regression models controlling for weather and seasonal incidence fluctuations. We estimated age-specific annual peak timing and compared influenza outcomes before, during and after school breaks. During the 118 university holiday weeks, average weekly tests were lower than in 140 school term weeks (5.93 vs. 11.99 cases/week, P < 0.005). The dampening of tests during Winter Break was evident in all ages and in those 5-24 years (RR = 0.31; 95% CI 0.22-0.41 vs. RR = 0.14; 95% CI 0.09-0.22, respectively). A significant increase in tests was observed during Spring Break in 45-64 years old adults (RR = 2.12; 95% CI 1.14-3.96). Milwaukee Public Schools holiday breaks showed similar amplification and dampening effects. Overall, calendar effects depend on the proximity and alignment of an individual holiday to age-specific and influenza outcome-specific peak timing. Better quantification of individual holiday effects, tailored to specific age groups, should improve influenza prevention measures.

Comprehensive US database and model for ethanol blend effects on regulated tailpipe emissions.

Particulate matter (PM), oxides of nitrogen (NOx), carbon monoxide (CO), and total hydrocarbons (THC) in gasoline exhaust affect atmospheric quality, and hence human health. Ethanol produced from corn grain is a renewable resource with favorable anti-knock properties for gasoline blending. Refiners alter petroleum composition to produce a finished blend that meets specifications. Ethanol blending affects emissions from market fuels both directly and indirectly since aromatics are typically removed from the BOB as ethanol is added to reach a constant octane rating. Numerous studies have been conducted to assess the effect of ethanol blending on light duty vehicle emissions. However, few studies have examined market fuel blends directly and small studies yield insufficient information to be generally applicable. If blending of fuels for a study does not yield gasoline that adequately resembles the composition of a market blend, the generalizability of study results may be impacted by nonlinear blending effects. Most vehicle-based fuel effect studies employed fuel formulations that either facilitate examination of several fuel variables or blend ethanol into a baseline gasoline (splash blending). Such study results do not support direct quantification of emissions inventory effects. To examine real world blending implications on regulated emissions [PM, NOx, CO, THC], we compiled a comprehensive database of US emission studies, developed regression models based on fuel and vehicle properties, and used those models to estimate differences in emissions from expected market fuel compositions. We addressed nonlinear responses to ethanol composition by modeling both low (up to 10% ethanol by volume) and mid blends (split models). We used the Federal Test Procedure (FTP) and Unified Cycle (LA92) driving schedule data, with the cold-start eliciting the highest emissions. PM cold-start emissions were lower with higher ethanol content, and more so at higher blend levels but hot-running emissions showed no differences with respect to ethanol level. For all emissions, the effects differed between port fuel injection (PFI) and gasoline direct injection (GDI) powered vehicles and for NOx, CO and THC there were differences between comphrehensive and split models. NOx results varied over blend levels and THC results were scattered for the higher blends. CO emissions were lower with higher ethanol content in nearly all cases for PFI but only the hot-running GDI. Results did not differ between summer regular and premium fuels. To the extent that PFI and GDI models differ, an emissions inventory calculation should treat them separately. There is uncertainty directly associated with the regression process, and with model inputs since study methods vary and compositions are reported differently between laboratories and test methods. Small changes in modeled emissions should be considered in this light.

Hospitalizations for Alcohol and Opioid Use Disorders in Older Adults: Trends, Comorbidities, and Differences by Gender, Race, and Ethnicity.

Background: The prevalence of substance use disorders (SUDs) among adults ages 65 and older has been increasing at a notably high rate in recent years, yet little information exists on hospitalizations for SUDs among this age group. In this study we examined trends in hospitalizations for alcohol use disorders (AUDs) and opioid use disorders (OUDs) among adults 65 and older in the United States, including differences by gender and race/ethnicity. Methods: We used Medicare claims data for years 2007-2014 from beneficiaries ages 65 and older. We abstracted hospitalization records with an ICD-9 diagnostic code for an AUD or OUD. Hospitalization rates were calculated using population estimates from the United States Census. We examined trends in quarterly hospitalization rates for hospitalizations with AUD/OUD as primary diagnoses, and separately for those with these disorders as secondary diagnoses. We also examined comorbidities for those with a primary diagnosis of AUD/OUD. Analyses were conducted for all hospitalizations with AUD/OUD diagnoses, and separately by gender and race/ethnicity. Results: Between the last quarter of 2007 and the third quarter of 2014, AUD hospitalization rates increased from 485 to 579 per million (19%), and OUD hospitalization rates from 46 to 101 per million (120%) and varied by gender (for AUD) and race/ethnicity (for both AUD and OUD). Hospitalization rates were particularly high for Black older adults, as was the increase in hospitalization rates. The increase in hospitalization rates was substantially higher for hospitalizations with AUD (84%) and OUD (269%) as secondary diagnoses. Conclusions: Hospitalizations for AUDs and OUDs among older adults increased at an alarming rate during the observation period, and disparities existed in hospitalization rates for these conditions. Interventions focusing on the needs of older adults with AUD and/or OUD are needed, particularly to address the needs of a growing racially/ethnically diverse older adult population.

Effects of Data Aggregation on Time Series Analysis of Seasonal Infections.

Time series analysis in epidemiological studies is typically conducted on aggregated counts, although data tend to be collected at finer temporal resolutions. The decision to aggregate data is rarely discussed in epidemiological literature although it has been shown to impact model results. We present a critical thinking process for making decisions about data aggregation in time series analysis of seasonal infections. We systematically build a harmonic regression model to characterize peak timing and amplitude of three respiratory and enteric infections that have different seasonal patterns and incidence. We show that irregularities introduced when aggregating data must be controlled during modeling to prevent erroneous results. Aggregation irregularities had a minimal impact on the estimates of trend, amplitude, and peak timing for daily and weekly data regardless of the disease. However, estimates of peak timing of the more common infections changed by as much as 2.5 months when controlling for monthly data irregularities. Building a systematic model that controls for data irregularities is essential to accurately characterize temporal patterns of infections. With the urgent need to characterize temporal patterns of novel infections, such as COVID-19, this tutorial is timely and highly valuable for experts in many disciplines.

An analecta of visualizations for foodborne illness trends and seasonality.

Disease surveillance systems worldwide face increasing pressure to maintain and distribute data in usable formats supplemented with effective visualizations to enable actionable policy and programming responses. Annual reports and interactive portals provide access to surveillance data and visualizations depicting temporal trends and seasonal patterns of diseases. Analyses and visuals are typically limited to reporting the annual time series and the month with the highest number of cases per year. Yet, detecting potential disease outbreaks and supporting public health interventions requires detailed spatiotemporal comparisons to characterize spatiotemporal patterns of illness across diseases and locations. The Centers for Disease Control and Prevention's (CDC) FoodNet Fast provides population-based foodborne-disease surveillance records and visualizations for select counties across the US. We offer suggestions on how current FoodNet Fast data organization and visual analytics can be improved to facilitate data interpretation, decision-making, and communication of features related to trend and seasonality. The resulting compilation, or analecta, of 436 visualizations of records and codes are openly available online.

Agglomerative Clustering of Enteric Infections and Weather Parameters to Identify Seasonal Outbreaks in Cold Climates.

The utility of agglomerative clustering methods for understanding dynamic systems that do not have a well-defined periodic structure has not yet been explored. We propose using this approach to examine the association between disease and weather parameters, to compliment the traditional harmonic regression models, and to determine specific meteorological conditions favoring high disease incidence. We utilized daily records on reported salmonellosis and non-specific enteritis, and four meteorological parameters (ambient temperature, dew point, humidity, and barometric pressure) in Barnaul, Russia in 2004-2011, maintained by the CliWaDIn database. The data structure was examined using the t-distributed stochastic neighbor embedding (t-SNE) method. The optimal number of clusters was selected based on Ward distance using the silhouette metric. The selected clusters were assessed with respect to their density and homogeneity. We detected that a well-defined cluster with high counts of salmonellosis occurred during warm summer days and unseasonably warm days in spring. We also detected a cluster with high counts of non-specific enteritis that occurred during unusually "very warm" winter days. The main advantage offered by the proposed technique is its ability to create a composite of meteorological conditions-a rule of thumb-to detect days favoring infectious outbreaks for a given location. These findings have major implications for understanding potential health impacts of climate change.

Use of Air-activated Thermal Devices during Recovery after Surgery in Mice.

Laboratory mice (Mus musculus) are susceptible to hypothermia, especially during anesthetic events, disease states, and exposure to environmental stressors. Thermal support devices for small mammals are numerous, but often require a power source and may be impractical to use for cages on a rack. Air-activated thermal devices (AATD) are mixtures of chemicals that cause an exothermic reaction. In this study, we examined the environmental effects of AATD on internal cage temperatures without the use of additional equipment as well as the physiologic effects of AATD as postoperative thermal support in mice. For environmental experiments, temperatures measured inside the cage and above the AATD peaked at 35.6 ± 2.5 °C (13.4 °C higher than control cages). We also demonstrated that the amount of heat produced by AATD and its temporal distribution are dependent on cage and rack types. For physiologic experiments, mice were surgically implanted with an intraperitoneal temperature telemetry device in a static cage setting. Recovery times and final body temperature at 5 h postoperatively did not differ significantly between mice with and without AATD. During the first 0 to 3 h after mice returned to their home cages, body temperature dropped markedly in mice without AATD but not in mice with AATD. Based on this result the physiologic results of our study support that AATD can be useful in providing extended thermal support for mice housed in static microisolation cages to help maintain body temperature postsurgically. Environmental results of our studies demonstrated that AATD provide local clinically relevant thermal support for 2.5 to 6 h, depending on cage set-up.

Quantifying tap-to-household water quality deterioration in urban communities in Vellore, India: The impact of spatial assumptions.

Municipal water sources in India have been found to be highly contaminated, with further water quality deterioration occurring during household storage. Quantifying water quality deterioration requires knowledge about the exact source tap and length of water storage at the household, which is not usually known. This study presents a methodology to link source and household stored water, and explores the effects of spatial assumptions on the association between tap-to-household water quality deterioration and enteric infections in two semi-urban slums of Vellore, India. To determine a possible water source for each household sample, we paired household and tap samples collected on the same day using three spatial approaches implemented in GIS: minimum Euclidean distance; minimum network distance; and inverse network-distance weighted average. Logistic and Poisson regression models were used to determine associations between water quality deterioration and household-level characteristics, and between diarrheal cases and water quality deterioration. On average, 60% of households had higher fecal coliform concentrations in household samples than at source taps. Only the weighted average approach detected a higher risk of water quality deterioration for households that do not purify water and that have animals in the home (RR=1.50 [1.03, 2.18], p=0.033); and showed that households with water quality deterioration were more likely to report diarrheal cases (OR=3.08 [1.21, 8.18], p=0.02). Studies to assess contamination between source and household are rare due to methodological challenges and high costs associated with collecting paired samples. Our study demonstrated it is possible to derive useful spatial links between samples post hoc; and that the pairing approach affects the conclusions related to associations between enteric infections and water quality deterioration.