BackgroundA range of health outcomes at a population level are related to differences in levels of social disadvantage. Understanding the impact of any such differences in palliative care is important. The aim of this study was to assess, by level of socio-economic disadvantage, referral patterns to specialist palliative care and proximity to inpatient services.MethodsAll inpatient and community palliative care services nationally were geocoded (using postcode) to one nationally standardised measure of socio-economic deprivation – Socio-Economic Index for Areas (SEIFA; 2006 census data). Referral to palliative care services and characteristics of referrals were described through data collected routinely at clinical encounters. Inpatient location was measured from each person’s home postcode, and stratified by socio-economic disadvantage.ResultsThis study covered July – December 2009 with data from 10,064 patients. People from the highest SEIFA group (least disadvantaged) were significantly less likely to be referred to a specialist palliative care service, likely to be referred closer to death and to have more episodes of inpatient care for longer time. Physical proximity of a person’s home to inpatient care showed a gradient with increasing distance by decreasing levels of socio-economic advantage.ConclusionThese data suggest that a simple relationship of low socioeconomic status and poor access to a referral-based specialty such as palliative care does not exist. Different patterns of referral and hence different patterns of care emerge.
Walsh, JA, Dawber, JP, Lepers, R, Brown, M, and Stapley, PJ. Is moderate intensity cycling sufficient to induce cardiorespiratory and biomechanical modifications of subsequent running? J Strength Cond Res 31(4): 1078–1086, 2017—This study sought to determine whether prior moderate intensity cycling is sufficient to influence the cardiorespiratory and biomechanical responses during subsequent running. Cardiorespiratory and biomechanical variables measured after moderate intensity cycling were compared with control running at the same intensity. Eight highly trained, competitive triathletes completed 2 separate exercise tests; (a) a 10-minute control run (no prior cycling) and, (b) a 30-minute transition run (TR) (preceded by 20-minute of variable cadence cycling, i.e., run versus cycle-run). Respiratory, breathing frequency (f b), heart rate (HR), cost of running (Cr), rate constant, stride length, and stride frequency variables were recorded, normalized, and quantified at the mean response time (MRT), third minute, 10th minute (steady state), and overall for the control run (CR) and TR. Cost of running increased (p ≤ 0.05) at all respective times during the TR. The V̇E/V̇co 2 and respiratory exchange ratio (RER) were significantly (p < 0.01) elevated at the MRT and 10th minute of the TR. Furthermore, overall mean increases were recorded for Cr, V̇E, V̇E/V̇co 2, RER, f b (p < 0.01), and HR (p ≤ 0.05) during the TR. Rate constant values for oxygen uptake were significantly different between CR and TR (0.48 ± 0.04 vs. 0.89 ± 0.15; p < 0.01). Stride length decreased across all recorded points during the TR (p ≤ 0.05) and stride frequency increased at the MRT and 3 minutes (p < 0.01). The findings suggest that at moderate intensity, prior cycling influences the cardiorespiratory response during subsequent running. Furthermore, prior cycling seems to have a sustained effect on the Cr during subsequent running.
Summary Small area estimation typically requires model‐based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M‐quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.
Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.
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