Prioritising public health: a qualitative study of decision making to reduce health inequalities

<p>Abstract</p> <p>Background</p> <p>The public health system in England is currently facing dramatic change. Renewed attention has recently been paid to the best approaches for tackling the health inequalities which remain entrenched within British society and across the globe. In order to consider the opportunities and challenges facing the new public health system in England, we explored the current experiences of those involved in decision making to reduce health inequalities, taking cardiovascular disease (CVD) as a case study.</p> <p>Methods</p> <p>We conducted an in-depth qualitative study employing 40 semi-structured interviews and three focus group discussions. Participants were public health policy makers and planners in CVD in the UK, including: Primary Care Trust and Local Authority staff (in various roles); General Practice commissioners; public health academics; consultant cardiologists; national guideline managers; members of guideline development groups, civil servants; and CVD third sector staff.</p> <p>Results</p> <p>The short term target- and outcome-led culture of the NHS and the drive to achieve "more for less", combined with the need to address public demand for acute services often lead to investment in "downstream" public health intervention, rather than the "upstream" approaches that are most effective at reducing inequalities. Despite most public health decision makers wishing to redress this imbalance, they felt constrained due to difficulties in partnership working and the over-riding influence of other stakeholders in decision making processes. The proposed public health reforms in England present an opportunity for public health to move away from the medical paradigm of the NHS. However, they also reveal a reluctance of central government to contribute to shifting social norms.</p> <p>Conclusions</p> <p>It is vital that the effectiveness and cost effectiveness of all new and existing policies and services affecting public health are measured in terms of their impact on the social determinants of health and health inequalities. Researchers have a vital role to play in providing the complex evidence required to compare different models of prevention and service delivery. Those working in public health must develop leadership to raise the profile of health inequalities as an issue that merits attention, resources and workforce capacity; and advocate for central government to play a key role in shifting social norms.</p

Can ethograms be automatically generated using body acceleration data from free-ranging birds?

An ethogram is a catalogue of discrete behaviors typically employed by a species. Traditionally animal behavior has been recorded by observing study individuals directly. However, this approach is difficult, often impossible, in the case of behaviors which occur in remote areas and/or at great depth or altitude. The recent development of increasingly sophisticated, animal-borne data loggers, has started to overcome this problem. Accelerometers are particularly useful in this respect because they can record the dynamic motion of a body in e.g. flight, walking, or swimming. However, classifying behavior using body acceleration characteristics typically requires prior knowledge of the behavior of free-ranging animals. Here, we demonstrate an automated procedure to categorize behavior from body acceleration, together with the release of a user-friendly computer application, “Ethographer”. We evaluated its performance using longitudinal acceleration data collected from a foot-propelled diving seabird, the European shag, Phalacrocorax aristotelis. The time series data were converted into a spectrum by continuous wavelet transformation. Then, each second of the spectrum was categorized into one of 20 behavior groups by unsupervised cluster analysis, using k-means methods. The typical behaviors extracted were characterized by the periodicities of body acceleration. Each categorized behavior was assumed to correspond to when the bird was on land, in flight, on the sea surface, diving and so on. The behaviors classified by the procedures accorded well with those independently defined from depth profiles. Because our approach is performed by unsupervised computation of the data, it has the potential to detect previously unknown types of behavior and unknown sequences of some behaviors