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  • The influence of maternally derived antibody and infant age at vaccination on infant vaccine responses: An individual participant meta-analysis

    3 July 2018

    © 2017 American Medical Association. All rights reserved. IMPORTANCE The design of infant immunization schedules requires an understanding of the factors that determine the immune response to each vaccine antigen. DATA SOURCES Deidentified individual participant data from GlaxoSmithKline clinical trials were obtained through Clinical Study Data Request. The data were requested on January 2, 2015, and final data were received on April 11, 2016. STUDY SELECTION Immunogenicity trials of licensed or unlicensed vaccines administered to infants were included if antibody concentrations in infants were measured prior to the first dose of vaccine. DATA EXTRACTION AND SYNTHESIS The databasewas examined; studies that appeared to have appropriate data were reviewed. MAIN OUTCOMES AND MEASURES Antigen-specific antibody concentration measured 1 month after priming vaccine doses, before booster vaccination, and 1 month after booster vaccine doses. RESULTS A total of 7630 infants from 32 studies in 17 countries were included. Mean (SD) age at baseline was 9.0 (2.3) weeks; 3906 (51.2%) were boys. Preexisting maternal antibody inhibited infant antibody responses to priming doses for 20 of 21 antigens. The largest effects were observed for inactivated polio vaccine, where 2-fold higher maternal antibody concentrations resulted in 20%to 28%lower postvaccination antibody concentration (geometric mean ratios [GMRs], type 1: 0.80; 95%CI, 0.78-0.83; type 2: 0.72; 95%CI, 0.69-0.74; type 3: 0.78; 95%CI, 0.75-0.82). For acellular pertussis antigens, 2-fold higher maternal antibody was associated with 11%lower postvaccination antibody for pertussis toxoid (GMR, 0.89; 95%CI, 0.87-0.90) and filamentous hemagglutinin (GMR, 0.89; 95%CI, 0.88-0.90) and 22%lower pertactin antibody (GMR, 0.78; 95%CI, 0.77-0.80). For tetanus and diphtheria, these estimates were 13%(GMR, 0.87; 95%CI, 0.86-0.88) and 24%(GMR, 0.76; 95%CI, 0.74-0.77), respectively. The influence of maternal antibody was still evident in reduced responses to booster doses of acellular pertussis, inactivated polio, and diphtheria vaccines at 12 to 24 months of age. Children who were older when first immunized had higher antibody responses to priming doses for 18 of 21 antigens, after adjusting for the effect of maternal antibody concentrations. The largest effect was seen for polyribosylribitol phosphate antibody, where responses were 71%higher per month (GMR, 1.71; 95%CI, 1.52-1.92). CONCLUSIONS AND RELEVANCE Maternal antibody concentrations and infant age at first vaccination both influence infant vaccine responses. These effects are seen for almost all vaccines contained in global immunization programs and influence immune response for some vaccines even at the age of 24 months. These data highlight the potential for maternal immunization strategies to influence established infant programs.

  • Corrigendum to “Persistence of bactericidal antibodies following booster vaccination with 4CMenB at 12, 18 or 24 months and immunogenicity of a fifth dose administered at 4 years of age-a phase 3 extension to a randomised controlled trial” (Vaccine (2017) 35(2) (395–402) (S0264410X16310404) (10.1016/j.vaccine.2016.11.009))

    3 July 2018

    © 2017 The Author(s) The authors regret that since the publication of this paper, they have discovered an error in the Conclusion of the Abstract. The corrected Conclusion is listed below. The authors would also like to correct the name of Dr. Miguel Tortajada-Girbés in The European Men B vaccine study group section. Conclusion: Waning of protective antibodies occurred 24–36 months after toddler booster regardless of age at boost. This was least marked against target strains 5/99 and M10713. A robust memory response occurred after a booster dose given at 4 years of age. The authors would like to apologise for any inconvenience caused.

  • How can point-of-care HbA1c testing be integrated into UK primary care consultations? – A feasibility study

    3 July 2018

    © 2017 The Authors Aims Point-of-care (POC) HbA1c testing gives a rapid result, allowing testing and treatment decisions to take place in a single appointment. Trials of POC testing have not been shown to improve HbA1c, possibly because of how testing was implemented. This study aimed to identify key components of POC HbA1c testing and determine strategies to optimise implementation in UK primary care. Methods This cohort feasibility study recruited thirty patients with type 2 diabetes and HbA1c > 7.5% (58 mmol/mol) into three primary care clinics. Patients’ clinical care included two POC HbA1c tests over six months. Data were collected on appointment duration, clinical decisions, technical performance and patient behaviour. Results Fifty-three POC HbA1c consultations took place during the study; clinical decisions were made in 30 consultations. Five POC consultations with a family doctor lasted on average 11 min and 48 consultations with nurses took on average 24 min. Five POC study visits did not take place in one clinic. POC results were uploaded to hospital records from two clinics. In total, sixty-three POC tests were performed, and there were 11 cartridge failures. No changes in HbA1c or patient behaviour were observed. Conclusions HbA1c measurement with POC devices can be effectively implemented in primary care. This work has identified when these technologies might work best, as well as potential challenges. The findings can be used to inform the design of a pragmatic trial to implement POC HbA1c testing.

  • Using the Medical Research Council framework for development and evaluation of complex interventions in a low resource setting to develop a theory-based treatment support intervention delivered via SMS text message to improve blood pressure control

    3 July 2018

    © 2018 The Author(s). Background: Several frameworks now exist to guide intervention development but there remains only limited evidence of their application to health interventions based around use of mobile phones or devices, particularly in a low-resource setting. We aimed to describe our experience of using the Medical Research Council (MRC) Framework on complex interventions to develop and evaluate an adherence support intervention for high blood pressure delivered by SMS text message. We further aimed to describe the developed intervention in line with reporting guidelines for a structured and systematic description. Methods: We used a non-sequential and flexible approach guided by the 2008 MRC Framework for the development and evaluation of complex interventions. Results: We reviewed published literature and established a multi-disciplinary expert group to guide the development process. We selected health psychology theory and behaviour change techniques that have been shown to be important in adherence and persistence with chronic medications. Semi-structured interviews and focus groups with various stakeholders identified ways in which treatment adherence could be supported and also identified key features of well-regarded messages: polite tone, credible information, contextualised, and endorsed by identifiable member of primary care facility staff. Direct and indirect user testing enabled us to refine the intervention including refining use of language and testing of interactive components. Conclusions: Our experience shows that using a formal intervention development process is feasible in a low-resource multi-lingual setting. The process enabled us to pre-test assumptions about the intervention and the evaluation process, allowing the improvement of both. Describing how a multi-component intervention was developed including standardised descriptions of content aimed to support behaviour change will enable comparison with other similar interventions and support development of new interventions. Even in low-resource settings, funders and policy-makers should provide researchers with time and resources for intervention development work and encourage evaluation of the entire design and testing process. Trial registration: The trial of the intervention is registered with South African National Clinical Trials Register number (SANCTR DOH-27-1212-386; 28/12/2012); Pan Africa Trial Register (PACTR201411000724141; 14/12/2013); ClinicalTrials.gov (NCT02019823; 24/12/2013).

  • The conceptualization of a Just-In-Time Adaptive Intervention (JITAI) for the reduction of sedentary behavior in older adults.

    29 June 2018

    Low physical activity and high sedentary behavior in older adults can be addressed with interventions that are delivered through modern technology. Just-In-Time Adaptive Interventions (JITAIs) are an emerging technology-driven behavior-change intervention type and capitalize on data that is collected via mobile sensing technology (e.g., smartphones) to trigger appropriate support in real-life. In this paper we integrated behavior change and aging theory and research as well as knowledge around older adult's technology use to conceptualize a JITAI targeting the reduction of sedentary behavior in older adults. The JITAIs ultimate goal is to encourage older adults to take regular activity breaks from prolonged sitting. As a proximal outcome, we suggest the number of daily activity breaks from sitting. Support provided to interrupt sitting time can be based on tailoring variables: (I) the current accumulated sitting time; (II) the location of the individual; (III) the time of the day; (IV) the frequency of daily support prompts; and (V) the response to previous support prompts. Data on these variables can be collected using sensors that are commonly inbuilt into smartphones (e.g., accelerometer, GPS). Support prompts might be best delivered via traditional text messages as older adults are usually familiar and comfortable with this function. The content of the prompts should encourage breaks from prolonged sitting by highlighting immediate benefits of sitting time interruptions. Additionally, light physical activities that could be done during the breaks should also be presented (e.g., walking into the kitchen to prepare a cup of tea). Although the conceptualized JITAI can be developed and implemented to test its efficacy, more work is required to identify ways to collect, aggregate, organize and immediately use dense data on the proposed and other potentially important tailoring variables. Machine learning and other computational modelling techniques commonly used by computer scientists and engineers appear promising. With this, to develop powerful JITAIs and to actualize the full potential of modern sensing technologies transdisciplinary approaches are required.