Measuring Confidentiality Risks in Census Data

Two trends have been on a collision course over the recent past. The first is the increasing demand by researchers for greater detail and flexibility in outputs from the decennial Census of Population. The second is the need felt by the Census Offices to demonstrate more clearly that Census data have been explicitly protected from the risk of disclosure of information about individuals. To reconcile these competing trends the authors propose a statistical measure of risks of disclosure implicit in the release of aggregate census data. The ideas of risk measurement are first developed for microdata where there is prior experience and then modified to measure risk in tables of counts. To make sure that the theoretical ideas are fully expounded, the authors develop small worked example. The risk measure purposed here is currently being tested out with synthetic and a real Census microdata. It is hoped that this approach will both refocus the census confidentiality debate and contribute to the safe use of user defined flexible census output geographies

Emerging drugs for respiratory syncytial virus infection

Although respiratory syncytial virus (RSV) was discovered > 40 years ago, treatment remains largely supportive. There are no safe and effective vaccines or specific treatments other than prophylaxis with passive antibody therapy (palivizumab). However, there are good reasons to think that the scene may soon change. As the pace of development of anti-viral drugs accelerates and optimism over vaccines increases, novel therapies are set to make a major impact in the management of this very common infection. The use and effect of such interventions are not easy to anticipate, but could ultimately include the interruption of RSV's transmission resulting in profound changes to the impact of RSV on human health

Applications of Population Counts Based on Administrative Data at Local Level

The House of Commons Treasury Committee (2008) identified the three main purposes of population statistics as resource allocation, population ratios and local planning and intelligence, and described current population estimates as ‘unfit for purpose’. This paper considers the limitations of current population statistics in the context of these purposes and how using administrative data available at local authority level can overcome them. A sequel to a companion paper in this journal dealing with the population counting methodology, it considers how administrative data can be captured and organised at a local level for the benefit of local service providers and communities. A detailed example based on one London local authority shows the results of this approach in applications related to the three purposes given. A concluding section reflects on the wider applicability of the approach

The comparative clinical course of pregnant and non-pregnant women hospitalised with influenza A(H1N1)pdm09 infection

Introduction: The Influenza Clinical Information Network (FLU-CIN) was established to gather detailed clinical and epidemiological information about patients with laboratory confirmed A(H1N1)pdm09 infection in UK hospitals. This report focuses on the clinical course and outcomes of infection in pregnancy.Methods: A standardised data extraction form was used to obtain detailed clinical information from hospital case notes and electronic records, for patients with PCR-confirmed A(H1N1)pdm09 infection admitted to 13 sentinel hospitals in five clinical 'hubs' and a further 62 non-sentinel hospitals, between 11th May 2009 and 31st January 2010.Outcomes were compared for pregnant and non-pregnant women aged 15-44 years, using univariate and multivariable techniques.Results: Of the 395 women aged 15-44 years, 82 (21%) were pregnant; 73 (89%) in the second or third trimester. Pregnant women were significantly less likely to exhibit severe respiratory distress at initial assessment (OR?=?0.49 (95% CI: 0.30-0.82)), require supplemental oxygen on admission (OR?=?0.40 (95% CI: 0.20-0.80)), or have underlying co-morbidities (p-trend <0.001). However, they were equally likely to be admitted to high dependency (Level 2) or intensive care (Level 3) and/or to die, after adjustment for potential confounders (adj. OR?=?0.93 (95% CI: 0.46-1.92). Of 11 pregnant women needing Level 2/3 care, 10 required mechanical ventilation and three died.Conclusions: Since the expected prevalence of pregnancy in the source population was 6%, our data suggest that pregnancy greatly increased the likelihood of hospital admission with A(H1N1)pdm09. Pregnant women were less likely than non-pregnant women to have respiratory distress on admission, but severe outcomes were equally likely in both groups

Therapeutic blockade of granulocyte macrophage colony-stimulating factor in COVID-19-associated hyperinflammation: challenges and opportunities

The COVID-19 pandemic is a global public health crisis, with considerable mortality and morbidity exerting pressure on health-care resources, including critical care. An excessive host inflammatory response in a subgroup of patients with severe COVID-19 might contribute to the development of acute respiratory distress syndrome (ARDS) and multiorgan failure. Timely therapeutic intervention with immunomodulation in patients with hyperinflammation could prevent disease progression to ARDS and obviate the need for invasive ventilation. Granulocyte macrophage colony-stimulating factor (GM-CSF) is an immunoregulatory cytokine with a pivotal role in initiation and perpetuation of inflammatory diseases. GM-CSF could link T-cell-driven acute pulmonary inflammation with an autocrine, self-amplifying cytokine loop leading to monocyte and macrophage activation. This axis has been targeted in cytokine storm syndromes and chronic inflammatory disorders. Here, we consider the scientific rationale for therapeutic targeting of GM-CSF in COVID-19-associated hyperinflammation. Since GM-CSF also has a key role in homoeostasis and host defence, we discuss potential risks associated with inhibition of GM-CSF in the context of viral infection and the challenges of doing clinical trials in this setting, highlighting in particular the need for a patient risk-stratification algorithm