Measuring change in health care equity using small-area administrative data - Evidence from the English NHS 2001-2008

This study developed a method for measuring change in socio-economic equity in health care utilisation using small-area level administrative data. Our method provides more detailed information on utilisation than survey data but only examines socio-economic differences between neighbourhoods rather than individuals. The context was the English NHS from 2001 to 2008, a period of accelerated expenditure growth and pro-competition reform. Hospital records for all adults receiving non-emergency hospital care in the English NHS from 2001 to 2008 were aggregated to 32,482 English small areas with mean population about 1500 and combined with other small-area administrative data. Regression models of utilisation were used to examine year-on-year change in the small-area association between deprivation and utilisation, allowing for population size, age-sex composition and disease prevalence including (from 2003 to 2008) cancer, chronic kidney disease, coronary heart disease, diabetes, epilepsy, hypertension, hypothyroidism, stroke, transient ischaemic attack and (from 2006 to 2008) atrial fibrillation, chronic obstructive pulmonary disease, obesity and heart failure. There was no substantial change in small-area associations between deprivation and utilisation for outpatient visits, hip replacement, senile cataract, gastroscopy or coronary revascularisation, though overall non-emergency inpatient admissions rose slightly faster in more deprived areas than elsewhere. Associations between deprivation and disease prevalence changed little during the period, indicating that observed need did not grow faster in more deprived areas than elsewhere. We conclude that there was no substantial deterioration in socio-economic equity in health care utilisation in the English NHS from 2001 to 2008, and if anything, there may have been a slight improvement.     

Waiting times and socioeconomic status: evidence from England

Waiting times for elective surgery, like hip replacement, are often referred to as an equitable rationing mechanism in publicly-funded healthcare systems because access to care is not based on socioeconomic status. Previous work has established that that this may not be the case and there is evidence of inequality in NHS waiting times favouring patients living in the least deprived neighbourhoods in England. We advance the literature by explaining variations of inequalities in waiting times in England in four different ways. First, we ask whether inequalities are driven by education rather than income. Our analysis shows that education and income deprivation have distinct effects on waiting time. Patients in the first quintile with least deprivation in education wait 9% less than patients in the second quintile and 14% less than patients in the third-to-fifth quintile. Patients in the fourth and fifth most income-deprived quintile wait about 7% longer than patients in the least deprived quintile. Second, we investigate whether inequalities arise "across" hospitals or "within" the hospital. The analysis provides evidence that most inequalities occur within hospitals rather than across hospitals. Moreover, failure to control for hospital fixed effects results in underestimation of the income gradient. Third, we explore whether inequalities arise across the entire waiting time distribution. Inequalities between better educated patients and other patients occur over large part of the waiting time distribution. Moreover we find that the education gradient becomes smaller for very long waiting. Fourth, we investigate whether the gradient may reflect the fact that patients with higher socioeconomic status have a different severity as proxied through a range of types and the number of diagnoses (in addition to age and gender) compared to those with lower socioeconomic status. We find no evidence that differences in severity explain the social gradient in waiting times.     

A Study of Cases of Hereditory Ectodermal Dysplasia - A Rare Dental Anomaly

Hereditary ectodermal dysplasia is a group of disorder running in the family where more than one manifestation occurs involving skin, nail, hair, glands and teeth In the present study, five cases were detected in district of Bangalore,Karnataka and studied in detail. Out of them three were girls and two boys showing manifestation dysplasia of teeth, skin & sweat glands between the age groups of 5 years to 14 years of age. There are three girls between 5 to 18 years showing oligodentia (0.13%) in 2 girls and anodentia in one girl (0.67%) associated with periorbital wrinkling and mild mid facial hypoplasia.The other 2 were boys between 8years and 15 years of age showed oligodentia, anhydosis brittle nails with vertical ridges, and 15 years old boy also showed periorbital wrinkling. The mothers of these five patients were also studied. Consanguity along with heredity and hypertension has played a vital role in the development of ectodermal dysplasia These 5 cases were compared and correlated with available literatures.     

Copy number variation leads to considerable diversity for B but not A haplotypes of the human KIR genes encoding NK cell receptors

The KIR complex appears to be evolving rapidly in humans, and more than 50 different haplotypes have been described, ranging from four to 14 KIR loci. Previously it has been suggested that most KIR haplotypes consist of framework genes, present in all individuals, which bracket a variable number of other genes. We used a new technique to type 793 families from the United Kingdom and United States for both the presence/absence of all individual KIR genes as well as copy number and found that KIR haplotypes are even more complex. It is striking that all KIR loci are subject to copy number variation (CNV), including the so-called framework genes, but CNV is much more frequent in KIR B haplotypes than KIR A haplotypes. These two basic KIR haplotype groups, A and B, appear to be following different evolutionary trajectories. Despite the great diversity, there are 11 common haplotypes, derived by reciprocal recombination near KIR2DL4, which collectively account for 94% of KIR haplotypes determined in Caucasian samples. These haplotypes could be derived from combinations of just three centromeic and two telomeric motifs, simplifying disease analysis for these haplotypes. The remaining 6% of haplotypes displayed novel examples of expansion and contraction of numbers of loci. Conventional KIR typing misses much of this additional complexity, with important implications for studying the genetics of disease association with KIR that can now be explored by CNV analysis.The killer immunoglobulin-like receptor (KIR) genes are part of the leukocyte receptor complex (LRC), on chromosome 19q13.4. These genes are highly polymorphic and occupy one of the most rapidly evolving regions of the human genome. A KIR complex is missing in the mouse genome, and even closely related primate species show substantial differences in organization (Parham et al. 2012).KIR molecules modulate the development and activity of natural killer (NK) and some T-cells through interaction with major histocompatibility complex (MHC) class I receptors. Genetic analysis comparing disease with control samples indicates that KIR variation, in conjunction with polymorphic MHC class I molecules, plays a key role both in human reproduction as well as in immune defense (Khakoo and Carrington 2006).Currently, 15 different KIR loci (KIR2DL1, KIR2DL2/KIR2DL3 [2DL2L3], KIR2DL4, KIR2DL5A, KIR2DL5B, KIR2DS1–5, KIR3DL1/KIR3DS1 [3DL1S1], KIR3DL2–3, and two pseudogenes, KIR2DP1 and KIR3DP1; all gene names abbreviated in subsequent text, without “KIR”) have been identified (http://www.ebi.ac.uk/ipd/kir/). Selected combinations of these genes are encoded on haplotypes within a 100- to 200-kb region of the LRC (Martin et al. 2000; Wilson et al. 2000). Most KIR haplotypes contain between seven and 12 genes plus the two pseudogenes. Based on this variation in gene content alone, more than 50 distinct KIR haplotypes have been identified (Hsu et al. 2002; Khakoo and Carrington 2006). Further complexity is introduced by extensive allelic variation, with more than 50 different alleles determined for some loci (Robinson et al. 2010).Previous analyses of these haplotypes suggested that they are subdivided into two distinct groups. Subsequently these two groups were shown to have differential associations with disease and reproductive success (Khakoo and Carrington 2006). Group A haplotypes comprise seven genes and two pseudogenes. Six of the seven KIRs are inhibitory in nature, because they have immunoreceptor tyrosine-based motifs (ITIMs) in their cytoplasmic tails. The seventh gene, KIR2DS4, is potentially activating but is disabled by a 22-bp frameshift deletion on ∼75% of A haplotypes and is only therefore functional in a minority of individuals (Hsu et al. 2002). Activating KIR genes do not contain ITIM motifs and instead are coupled to adaptor proteins, such as DAP12, which contain immunoreceptor tyrosine-based activating motifs (ITAMs).In contrast, Group B haplotypes are composed of variable numbers of KIR genes, and one or more of these are activating. This segregation into distinct and evolutionarily divergent haplotype groups (A and B) is unique to the human species (Parham et al. 2012). Furthermore, both haplotypic groups have been found in all human populations studied to date and are maintained by balancing selection (Gendzekhadze et al. 2009).The recent evolutionary plasticity of the KIRs and human-specific high diversity, in addition to their biomedical relevance, makes it important to study their organization. There is mounting evidence that receptor–ligand specificity between polymorphic KIR and polymorphic MHC class I influences susceptibility to infections, such as HIV and hepatitis C, in addition to cancer, autoimmune diseases, and disorders of pregnancy, as well as outcomes after hematological and solid organ transplantation (Parham 2005).The genomic organization of KIR haplotypes is structured relative to the fixed position of the so-called framework genes, 3DL3, 2DL4, and 3DL2 (Robinson et al. 2010). This gene order has been determined from the mapping and sequencing of several prototypic KIR haplotypes (Pyo et al. 2010). 3DL3 and 3DL2 mark the centromeric and telomeric boundaries of the cluster, respectively. 3DP1 and 2DL4 are placed centrally. Between 3DP1 and 2DL4 is a recombination hotspot. Annotating the haplotype motif structures centromeric (Cen) and telomeric (Tel) of this hotspot based on gene content (Pyo et al. 2010) has simplified and strengthened analysis in disease association studies (Hiby et al. 2010).It has been proposed that the arrangement of KIR genes in close head-to-tail orientation and their high sequence similarity facilitates gene gain and loss or copy number variation (CNV) by unidirectional alignment and sequential non-allelic homologous recombination (NAHR) (Martin et al. 2003). Consistent with this, unusual KIR haplotypes that possess aberrant gene content and fusion genes have been identified (Norman et al. 2002, 2007, 2009; Martin et al. 2008; Traherne et al. 2010).Given this background, we set out to explore variation in KIR haplotypes that may not be detected by conventional typing methods. To do this, we measured copy number as well as KIR presence or absence, using DNA from family-based cohorts in order to ensure the provenance of each haplotype.     

ACR guidance document on MR safe practices: 2013

Because there are many potential risks in the MR environment and reports of adverse incidents involving patients, equipment and personnel, the need for a guidance document on MR safe practices emerged. Initially published in 2002, the ACR MR Safe Practices Guidelines established de facto industry standards for safe and responsible practices in clinical and research MR environments. As the MR industry changes the document is reviewed, modified and updated. The most recent version will reflect these changes. J. Magn. Reson. Imaging 2013;37:501–530. © 2013 Wiley Periodicals, Inc.