Intradermal fractional-dose inactivated polio vaccine (fIPV) adjuvanted with double mutant Enterotoxigenic Escherichia coli heat labile toxin (dmLT) is well-tolerated and augments a systemic immune response to all three poliovirus serotypes in a randomized placebo-controlled trial.

Eradication of poliomyelitis globally is constrained by fecal shedding of live polioviruses, both wild-type and vaccine-derived strains, into the environment. Although inactivated polio vaccines (IPV) effectively protect the recipient from clinical poliomyelitis, fecal shedding of live virus still occurs following infection with either wildtype or vaccine-derived strains of poliovirus. In the drive to eliminate the last cases of polio globally, improvements in both oral polio vaccines (OPV) (to prevent reversion to virulence) and injectable polio vaccines (to improve mucosal immunity and prevent viral shedding) are underway. The E. coli labile toxin with two or "double" attenuating mutations (dmLT) may boost immunologic responses to IPV, including at mucosal sites. We performed a double-blinded phase I controlled clinical trial to evaluate safety, tolerability, as well as systemic and mucosal immunogenicity of IPV adjuvanted with dmLT, given as a fractional (1/5th) dose intradermally (fIPV-dmLT). Twenty-nine volunteers with no past exposure to OPV were randomized to a single dose of fIPV-dmLT or fIPV alone. fIPV-dmLT was well tolerated, although three subjects had mild but persistent induration and hyperpigmentation at the injection site. A ≥ 4-fold rise in serotype-specific neutralizing antibody (SNA) titers to all three serotypes was seen in 84% of subjects receiving fIPV-dmLT vs. 50% of volunteers receiving IPV alone. SNA titers were higher in the dmLT-adjuvanted group, but only differences in serotype 1 were significant. Mucosal immune responses, as measured by polio serotype specific fecal IgA were minimal in both groups and differences were not seen. fIPV-dmLT may offer a benefit over IPV alone. Beyond NAB responses protecting the individual, studies demonstrating the ability of fIPV-dmLT to prevent viral shedding are necessary. Studies employing controlled human infection models, using monovalent OPV post-vaccine are ongoing. Studies specifically in children may also be necessary and additional biomarkers of mucosal immune responses in this population are needed. Clinicaltrials.gov Identifer: NCT03922061.

A primary care learning collaborative to improve office systems and clinical management of pediatric asthma.

OBJECTIVE: Pediatric asthma is a common, relapsing-remitting, chronic inflammatory airway disease that when uncontrolled often leads to substantial patient and health care system burden. Improving management of asthma in primary care can help patients stay well controlled. METHODS: The Vermont Child Health Improvement Program (VCHIP) developed a quality improvement (QI) learning collaborative with a primary objective to improve clinical asthma management measures through improvement in primary care office systems to support asthma care. Seven months of medical record review data were evaluated for improvements on eight clinical asthma management measures. Pre and post office systems inventory (OSI) self-assessments detailing adherence to improvement strategies were analyzed for improvement. Logistic regressions were used to test for associations between OSI strategy post scores and the corresponding clinical asthma management measures by month seven. RESULTS: This study found significant improvement from baseline to month seven on seven of the eight clinical asthma management measures and between pre and post OSI for seven of the nine strategies assessed (N = 19 practices). Additionally, one point higher average OSI scores on the assessment and monitoring of asthma severity, asthma control, asthma action plans, and asthma education strategies were associated with significantly greater odds of improvement in their respective clinical asthma management measures. CONCLUSIONS: A QI learning collaborative approach in primary care can improve office systems and corresponding clinical management measures for pediatric patients with asthma. This suggests that linking specific office systems strategies to clinical measures may be a helpful tactic within the learning collaborative model.

Sparse modeling of spatial environmental variables associated with asthma.

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

Estimating Wisconsin asthma prevalence using clinical electronic health records and public health data.

OBJECTIVES: We compared a statewide telephone health survey with electronic health record (EHR) data from a large Wisconsin health system to estimate asthma prevalence in Wisconsin. METHODS: We developed frequency tables and logistic regression models using Wisconsin Behavioral Risk Factor Surveillance System and University of Wisconsin primary care clinic data. We compared adjusted odds ratios (AORs) from each model. RESULTS: Between 2007 and 2009, the EHR database contained 376,000 patients (30,000 with asthma), and 23,000 (1850 with asthma) responded to the Behavioral Risk Factor Surveillance System telephone survey. AORs for asthma were similar in magnitude and direction for the majority of covariates, including gender, age, and race/ethnicity, between survey and EHR models. The EHR data had greater statistical power to detect associations than did survey data, especially in pediatric and ethnic populations, because of larger sample sizes. CONCLUSIONS: EHRs can be used to estimate asthma prevalence in Wisconsin adults and children. EHR data may improve public health chronic disease surveillance using high-quality data at the local level to better identify areas of disparity and risk factors and guide education and health care interventions.