Conectar
The effectiveness of antibiotics for treating gonococcal infections is compromised due to escalating antibiotic resistance; and the development of an effective gonococcal vaccine has been challenging. Emerging evidence suggests that the licensed meningococcal B (MenB) vaccine, 4CMenB is effective against gonococcal infections due to cross-reacting antibodies and 95% genetic homology between the two bacteria, Neisseria meningitidis and Neisseria gonorrhoeae, that cause the diseases. This project aims to undertake epidemiological and genomic surveillance to evaluate the long-term protection of the 4CMenB vaccine against gonococcal infections in the Northern Territory (NT) and South Australia (SA), and to determine the potential benefit of a booster vaccine doses to provide longer-term protection against gonococcal infections.
This observational study will provide long-term evaluation results of the effectiveness of the 4CMenB vaccine against gonococcal infections at 4–7 years post 4CMenB programme implementation. Routine notifiable disease notifications will be the basis for assessing the impact of the vaccine on gonococcal infections. Pathology laboratories will provide data on the number and percentage of N. gonorrhoeae positive tests relative to all tests administered and will coordinate molecular sequencing for isolates. Genome sequencing results will be provided by SA Pathology and Territory Pathology/New South Wales Health Pathology, and linked with notification data by SA Health and NT Health. There are limitations in observational studies including the potential for confounding. Confounders will be analysed separately for each outcome/comparison.
The protocol and all study documents have been reviewed and approved by the SA Department for Health and Well-being Human Research Ethics Committee (HREC/2022/HRE00308), and the evaluation will commence in the NT on receipt of approval from the NT Health and Menzies School of Health Research Human Research Ethics Committee. Results will be published in peer-reviewed journals and presented at scientific meetings and public forums.
by Nataliya Rybnikova, Dani Broitman, Murielle Mary-Krause, Maria Melchior, Yakov Ben-Haim
Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample’s status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.
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