Risk of Cardiovascular Events and Death—Does Insurance Matter?

BACKGROUND: Many Americans lack health insurance. Despite good evidence that lack of insurance compromises access to care, few prospective studies examine its relationship to health outcomes. OBJECTIVE: To determine the relationship between insurance and cardiovascular outcomes and the relationship between insurance and selected process measures. DESIGN AND PARTICIPANTS: We used data from 15,792 participants in the Atherosclerosis Risk in Communities Study, a prospective cohort study. Participants were enrolled in 1987–1989 and returned for follow-up visits every 3 years, for a total of 4 visits. MAIN OUTCOME MEASURES: We estimated the hazard of myocardial infarction, stroke, and death associated with insurance status using Cox proportional hazard modeling. We used generalized estimating equations to examine the association between insurance status and risk of (1) reporting no routine physical examinations, (2) being unaware of a personal cardiovascular risk condition, and (3) inadequate control of cardiovascular risk conditions. RESULTS: Persons without insurance had higher rates of stroke (adjusted hazard ratio, 95% CI 1.22–2.22) and death (adjusted hazard ratio 1.26, 95% CI 1.03–1.53), but not myocardial infarction, than those who were insured. The uninsured were less likely to report routine physical examinations (adjusted risk ratio 1.13, 95% CI 1.08–1.18); more likely to be unaware of hypertension (adjusted risk ratio 1.12, 95% CI 1.00–1.25) and hyperlipidemia (adjusted risk ratio 1.11, 95% CI 1.03–1.19); and more likely to have poor blood pressure control (adjusted risk ratio 1.23, 95% CI 1.08–1.39). CONCLUSIONS: Lack of health insurance is associated with increased rates of stroke and death and with less awareness and control of cardiovascular risk conditions. Health insurance may improve cardiovascular risk factor awareness, control and outcomes

Validation of the Edinburgh Claudication Questionnaire in 1st generation Black African-Caribbean and South Asian UK migrants: A sub-study to the Ethnic-Echocardiographic Heart of England Screening (E-ECHOES) study

<p>Abstract</p> <p>Background</p> <p>We determined the diagnostic accuracy of the Edinburgh Claudication Questionnaire (ECQ) in 1^stgeneration Black African-Caribbean UK migrants as previous diagnostic questionnaires have been found to be less accurate in this population. We also determined the diagnostic accuracy of translated versions of the ECQ in 1^stgeneration South Asian UK migrants, as this has not been investigated before.</p> <p>Methods</p> <p>Subjects were recruited from the Ethnic-Echocardiographic Heart of England Screening (E-ECHOES) study, a community based screening survey for heart failure in minority ethnic groups. Translated versions of the ECQ were prepared following a recognised protocol. All participants attending screening between October 2007 and February 2009 were asked to complete the ECQ in the language of their choice (English, Punjabi, Bengali, Urdu, Hindi or Gujarati). Subjects answering positively to experiencing leg pain or discomfort on walking were asked to return to have Ankle Brachial Pressure Index (ABPI) measured.</p> <p>Results</p> <p>154 out of 2831 subjects participating in E-ECHOES (5.4%) were eligible to participate in this sub-study, for which 74.3% returned for ABPI assessment. Non-responders were younger than participants (59[9] vs. 65[11] years; p = 0.015). Punjabi, English and Bengali questionnaires identified participants with Intermittent Claudication, so these questionnaires were assessed. The sensitivities (SN), specificities (SP), positive (PPV) and negative (NPV) predictive values were calculated. English: SN: 50%; SP: 68%; PPV: 43%; NPV: 74%. Punjabi: SN: 50%; SP: 87%; PPV: 43%; NPV: 90%. Bengali: SN: 33%; SP: 50%; PPV: 13%; NPV: 73%. There were significant differences in diagnostic accuracy between the 3 versions (Punjabi: 83.8%; Bengali: 45%; English: 62.2%; p < 0.0001). No significant differences were found in sensitivity and specificity between illiterate and literate participants in any of the questionnaires and there was no significant different difference between those under and over 60 years of age.</p> <p>Conclusions</p> <p>Our findings suggest that the ECQ is not as sensitive or specific a diagnostic tool in 1^stgeneration Black African-Caribbean and South Asian UK migrants than in the Edinburgh Artery Study, reflecting the findings of other diagnostic questionnaires in these minority ethnic groups. However this study is limited by sample size so conclusions should be interpreted with caution.</p

Re-evaluating early breast neoplasia

Historically, histomorphological and epidemiological data suggested that atypical ductal hyperplasia and ductal carcinoma in situ are the earliest recognizable neoplastic stages of breast cancer progression. Over the past several years, detailed high-throughput molecular genetic, gene expression and epigenetic analyses have enhanced our understanding of these early neoplastic lesions and have re-shaped our view of human breast cancer progression to include multiple distinct pathways of evolution

Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors

Genomic DNA copy number alterations are key genetic events in the development and progression of human cancers. Here we report a genome-wide microarray comparative genomic hybridization (array CGH) analysis of DNA copy number variation in a series of primary human breast tumors. We have profiled DNA copy number alteration across 6,691 mapped human genes, in 44 predominantly advanced, primary breast tumors and 10 breast cancer cell lines. While the overall patterns of DNA amplification and deletion corroborate previous cytogenetic studies, the high-resolution (gene-by-gene) mapping of amplicon boundaries and the quantitative analysis of amplicon shape provide significant improvement in the localization of candidate oncogenes. Parallel microarray measurements of mRNA levels reveal the remarkable degree to which variation in gene copy number contributes to variation in gene expression in tumor cells. Specifically, we find that 62% of highly amplified genes show moderately or highly elevated expression, that DNA copy number influences gene expression across a wide range of DNA copy number alterations (deletion, low-, mid- and high-level amplification), that on average, a 2-fold change in DNA copy number is associated with a corresponding 1.5-fold change in mRNA levels, and that overall, at least 12% of all the variation in gene expression among the breast tumors is directly attributable to underlying variation in gene copy number. These findings provide evidence that widespread DNA copy number alteration can lead directly to global deregulation of gene expression, which may contribute to the development or progression of cancer

Widening of Socioeconomic Inequalities in U.S. Death Rates, 1993–2001

Background: Socioeconomic inequalities in death rates from all causes combined widened from 1960 until 1990 in the U.S., largely because cardiovascular death rates decreased more slowly in lower than in higher socioeconomic groups. However, no studies have examined trends in inequalities using recent US national data. Methodology/Principal Findings: We calculated annual age-standardized death rates from 1993–2001 for 25–64 year old non-Hispanic whites and blacks by level of education for all causes and for the seven most common causes of death using death certificate information from 43 states and Washington, D.C. Regression analysis was used to estimate annual percent change. The inequalities in all cause death rates between Americans with less than high school education and college graduates increased rapidly from 1993 to 2001 due to both significant decreases in mortality from all causes, heart disease, cancer, stroke, and other conditions in the most educated and lack of change or increases among the least educated. For white women, the all cause death rate increased significantly by 3.2 percent per year in the least educated and by 0.7 percent per year in high school graduates. The rate ratio (RR) comparing the least versus most educated increased from 2.9 (95 % CI, 2.8–3.1) in 1993 to 4.4 (4.1–4.6) in 2001 among white men, from 2.1 (1.8–2.5) to 3.4 (2.9–3–9) in black men, and from 2.6 (2.4–2.7) to 3.8 (3.6–4.0) in white women. Conclusion: Socioeconomic inequalities in mortality are increasing rapidly due to continued progress by educated whit

Applying unmixing to gene expression data for tumor phylogeny inference

<p>Abstract</p> <p>Background</p> <p>While in principle a seemingly infinite variety of combinations of mutations could result in tumor development, in practice it appears that most human cancers fall into a relatively small number of "sub-types," each characterized a roughly equivalent sequence of mutations by which it progresses in different patients. There is currently great interest in identifying the common sub-types and applying them to the development of diagnostics or therapeutics. Phylogenetic methods have shown great promise for inferring common patterns of tumor progression, but suffer from limits of the technologies available for assaying differences between and within tumors. One approach to tumor phylogenetics uses differences between single cells within tumors, gaining valuable information about intra-tumor heterogeneity but allowing only a few markers per cell. An alternative approach uses tissue-wide measures of whole tumors to provide a detailed picture of averaged tumor state but at the cost of losing information about intra-tumor heterogeneity.</p> <p>Results</p> <p>The present work applies "unmixing" methods, which separate complex data sets into combinations of simpler components, to attempt to gain advantages of both tissue-wide and single-cell approaches to cancer phylogenetics. We develop an unmixing method to infer recurring cell states from microarray measurements of tumor populations and use the inferred mixtures of states in individual tumors to identify possible evolutionary relationships among tumor cells. Validation on simulated data shows the method can accurately separate small numbers of cell states and infer phylogenetic relationships among them. Application to a lung cancer dataset shows that the method can identify cell states corresponding to common lung tumor types and suggest possible evolutionary relationships among them that show good correspondence with our current understanding of lung tumor development.</p> <p>Conclusions</p> <p>Unmixing methods provide a way to make use of both intra-tumor heterogeneity and large probe sets for tumor phylogeny inference, establishing a new avenue towards the construction of detailed, accurate portraits of common tumor sub-types and the mechanisms by which they develop. These reconstructions are likely to have future value in discovering and diagnosing novel cancer sub-types and in identifying targets for therapeutic development.</p

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power