Health Insurance Coverage and the Macroeconomy

The primary objective of this paper is to improve our understanding of the historic relationship between state and national macroeconomic climate and the health insurance coverage of Americans. The secondary objective of this paper is to use the historic findings to estimate how the number of uninsured Americans changed during the 2001 recession, and to estimate whether to date enough people have gained health insurance during the current recovery to offset the losses during the recession. We conclude that the macroeconomy (measured by state unemployment rate and real gross state product) is correlated with the probability of men's health insurance coverage and that this correlation is only partly explained by changes in men's employment status. Counter-cyclical health insurance programs such as Medicaid and the State Children's Health Insurance Program seem to ensure that the health insurance coverage of women and children is insulated from macroeconomic changes. We estimate that 851,000 Americans, the vast majority of whom were adult men, lost health insurance due to macroeconomic conditions alone during the 2001 recession.

The Impact of the Macroeconomy on Health Insurance Coverage: Evidence from the Great Recession

This paper investigates the impact of the macroeconomy on the health insurance coverage of Americans using panel data from the Survey of Income and Program Participation (SIPP) for 2004-2010, a period that includes the Great Recession of 2007-09. We find that a one percentage point increase in the state unemployment rate is associated with a 1.67 percentage point (2.12%) reduction in the probability that men have health insurance; this effect is strongest among college-educated, white, and older (50-64 year old) men. We estimate that 9.3 million Americans, the vast majority of whom were adult men, lost health insurance due to a higher unemployment rate alone during the 2007-09 recession. We conclude with a discussion of how components of recent health care reform may influence this relationship in the future.health insurance, Medicaid, SCHIP, recession, unemployment

The Impact of the Macroeconomy on Health Insurance Coverage: Evidence from the Great Recession

This paper investigates the impact of the macroeconomy on the health insurance coverage of Americans using panel data from the Survey of Income and Program Participation (SIPP) for 2004-2010, a period that includes the Great Recession of 2007-09. We find that a one percentage point increase in the state unemployment rate is associated with a 1.67 percentage point (2.12%) reduction in the probability that men have health insurance; this effect is strongest among college-educated, white, and older (50-64 year old) men. For women and children, health insurance coverage is not significantly correlated with the unemployment rate, which may be the result of public health insurance acting as a social safety net. Compared to the previous recession, the health insurance coverage of men is more sensitive to the unemployment rate, which may be due to the nature of the Great Recession.

The Impact of Income on the Weight of Elderly Americans

This paper tests whether income affects the body weight and clinical weight classification of elderly Americans using a natural experiment that led otherwise identical retirees to receive significantly different Social Security payments based on their year of birth. We exploit this natural experiment by estimating models of instrumental variables using data from the National Health Interview Surveys. The model estimates rule out even moderate effects of income on weight and on the probability of being underweight or obese, especially for men.

HMM sampling and applications to gene finding and alternative splicing

The standard method of applying hidden Markov models to biological problems is to find a Viterbi (maximal weight) path through the HMM graph. The Viterbi algorithm reduces the problem of finding the most likely hidden state sequence that explains given observations, to a dynamic programming problem for corresponding directed acyclic graphs. For example, in the gene finding application, the HMM is used to find the most likely underlying gene structure given a DNA sequence. In this note we discuss the applications of sampling methods for HMMs. The standard sampling algorithm for HMMs is a variant of the common forward-backward and backtrack algorithms, and has already been applied in the context of Gibbs sampling methods. Nevetheless, the practice of sampling state paths from HMMs does not seem to have been widely adopted, and important applications have been overlooked. We show how sampling can be used for finding alternative splicings for genes, including alternative splicings that are conserved between genes from related organisms. We also show how sampling from the posterior distribution is a natural way to compute probabilities for predicted exons and gene structures being correct under the assumed model. Finally, we describe a new memory efficient sampling algorithm for certain classes of HMMs which provides a practical sampling alternative to the Hirschberg algorithm for optimal alignment. The ideas presented have applications not only to gene finding and HMMs but more generally to stochastic context free grammars and RNA structure prediction

Multiple Testing Methods For ChIP-Chip High Density Oligonucleotide Array Data

Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genome-wide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA. We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence or not using a scan statistic that takes into account the spatial structure of the data. Different multiple testing procedures are considered for controlling the family-wise error rate and false discovery rate. A nested-Bonferroni adjustment, that is more powerful than the traditional Bonferroni adjustment when the test statistics are dependent, is discussed. Simulation studies show that taking into account the spatial structure of the data substantially improves the sensitivity of the multiple testing procedures. Application of the proposed methods to ChIP-Chip data for transcription factor p53 identified many potential target binding regions along human chromosomes 21 and 22. Among these identified regions, 18% fall within a 3kb vicinity of the 5\u27UTR of a known gene or CpG island, 31% fall between the codon start site and the codon end site of a known gene but not inside an exon. More than half of these potential target sequences contain the p53 consensus binding site or very close matches to it. Moreover, these target segments include the 13 experimentally verified p53 binding regions of Cawley et al. (2004), as well as 49 additional regions that show higher hybridization signal than these 13 experimentally verified regions

A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data, using alcohol dependence phenotypes and electroencephalogram measures

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip(® )Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96–106 cM) and 10 (149–176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112–153 cM) and a region on chromosome 6 (169–185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best

'Getting to Know Me': The second phase roll-out of a staff training programme for supporting people with dementia in general hospitals

Objectives The aims were to evaluate a second phase roll-out of a dementia care training programme for general hospital staff and to further develop two outcome scales: the Confidence in Dementia scale for measuring confidence in working with people with dementia and the Knowledge in Dementia scale for measuring knowledge in dementia. Method Following a 'training the trainers' phase, the study involved the delivery of the 'Getting to Know Me' training programme to a large number of staff (n = 517) across three National Health Service (NHS) Trusts situated in North-West England. The impact of the programme was evaluated using a pre-post design which explored: (i) changes in confidence in dementia, (ii) changes in knowledge in dementia, and (iii) changes in beliefs about behaviours that challenge. Results Statistically significant change was identified between pre-post training on all outcome measures (Confidence in Dementia: eight point increase, p < 0.001; Knowledge in Dementia: two point increase p < 0.001; controllability beliefs scale: four point decrease, p < 0.001). Medium to large effect sizes were demonstrated on all outcome measures. The psychometric properties of the Confidence in Dementia and Knowledge in Dementia scales are reported. Conclusion Staff knowledge in dementia and confidence in working with people with dementia significantly increased following attendance at the training sessions. The findings are consistent with preliminary findings and strengthen current knowledge about the impact of dementia care training in general hospitals. The Confidence in Dementia and Knowledge in Dementia scales continue to demonstrate psychometrically sound properties and demonstrate utility in the field of dementia research.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by, and undertaken as part of, the Health Innovation and Education Cluster (HIEC) programme. The HIEC programme was developed to support the delivery of high quality health care, provide education and training, promote innovation in health care, and lead to adoption of research. One of the main aims of the HIEC programme was to produce sustainable change in practice within the NHS

Older adults place lower value on choice relative to young adults

Choice is highly valued in modern society, from the supermarket to the hospital; however, it remains unknown whether older and younger adults place the same value on increased choice. The current investigation tested whether 53 older ( M age = 75.44 years) versus 53 younger adults ( M age = 19.58 years) placed lower value on increased choice by examining the monetary amounts they were willing to pay for increased prescription drug coverage options — important given the recently implemented Medicare prescription drug program. Results indicate that older adults placed lower value on increasing choice sets relative to younger adults, who placed progressively higher value on increasingly larger choice sets. These results are discussed regarding their implications for theory and policy