Political adverse selection

We study how the politicization of policies designed to correct market failures can undermine their effectiveness. The Patient Protection and Affordable Care Act (ACA) was among the most politically divisive expansions of the US government. We examine whether partisanship distorted enrollment and market outcomes in the ACA insurance marketplaces. Controlling for observable characteristics and holding fixed plans and premiums available, Republicans enrolled less than Democrats and independents in ACA marketplace plans. Selection out of the ACA marketplaces was strongest among Republicans with lower expected healthcare costs, generating adverse selection. Computing enrollment and average cost with and without partisan differences, we find that this political adverse selection reduced enrollment by around three million people and raised average costs in the marketplaces, increasing the level of public spending necessary to provide subsidies to low-income enrollees by around $105 per enrollee per year. Lower enrollments and higher costs are concentrated in more Republican areas, potentially contributing to polarized views of the ACA

Hypertension, uncontrolled hypertension and resistant hypertension: prevalence, comorbidities and prescribed medications in 228,406 adults resident in urban areas. A population-based observational study

Although hypertension is the leading cause of cardiovascular disease and premature death worldwide, it remains difficult to control. The prevalence of uncontrolled and resistant hypertension (RH) may be underestimated and can reach up to 50% of all hypertensive patients. The aim of this observational study was to analyze the prevalence of hypertension, uncontrolled hypertension and RH, and their associations with risk factors or diseases in a large cohort of patients referred to primary care physician. In a population of 228406 adults, we only collected data from people with a diagnosis of arterial hypertension for a total of 43,526 patients. For this purpose, we used the MySQL database, run by Azalea.NET, built on the medical records of 150 General Practitioners (GPs). Patient data included sex, age, blood pressure (BP) values, number of antihypertensive drugs and presence of major cardiovascular comorbidities. We classified patients with RH as those treated with 3 different antihypertensive agents, with recorded BP & GE; 140/90 mmHg, or patients taking & GE; 4 medications. The prevalence of hypertension was 19.06%, that of resistant hypertension was 2.46% of the whole population and 20.85% of the hypertensive group. Thirteen thousand hundred, forty-six patients (30.20% of the hypertensive group) had uncontrolled BP (& GE; 140/90 mmHg), whereas 16,577 patients did not have BP measurements done in the last 2 years (38.09% of the hypertensive group). Patients with uncontrolled BP were mainly female, used less drugs and showed a lower prevalence of all major cardiovascular comorbidities, except for diabetes. Instead, patients with RH had a significantly higher prevalence of all considered comorbidities compared to those without RH. Our results evidence that a broad number of patients with hypertension, especially those without comorbidities or with a low number of antihypertensive drugs, do not achieve adequate BP control. To improve the clinical management of these patients it is very important to increase the collaboration between GPs and clinical specialists of hypertension

Dissecting Molecular Heterogeneity of Circulating Tumor Cells (CTCs) from Metastatic Breast Cancer Patients through Copy Number Aberration (CNA) and Single Nucleotide Variant (SNV) Single Cell Analysis

Circulating tumor cells' (CTCs) heterogeneity contributes to counteract their introduction in clinical practice. Through single-cell sequencing we aim at exploring CTC heterogeneity in metastatic breast cancer (MBC) patients. Single CTCs were isolated using DEPArray NxT. After whole genome amplification, libraries were prepared for copy number aberration (CNA) and single nucleotide variant (SNV) analysis and sequenced using Ion GeneStudio S5 and Illumina MiSeq, respectively. CTCs demonstrate distinctive mutational signatures but retain molecular traces of their common origin. CNA profiling identifies frequent aberrations involving critical genes in pathogenesis: gains of 1q (CCND1) and 11q (WNT3A), loss of 22q (CHEK2). The longitudinal single-CTC analysis allows tracking of clonal selection and the emergence of resistance-associated aberrations, such as gain of a region in 12q (CDK4). A group composed of CTCs from different patients sharing common traits emerges. Further analyses identify losses of 15q and enrichment of terms associated with pseudopodium formation as frequent and exclusive events. CTCs from MBC patients are heterogeneous, especially concerning their mutational status. The single-cell analysis allows the identification of aberrations associated with resistance, and is a candidate tool to better address treatment strategy. The translational significance of the group populated by similar CTCs should be elucidated

Sexual Orientation and Household Decision Making: Same-Sex Couples' Balance of Power and Labor Supply Choices

I estimate how intra-household bargaining affects gay and lesbian couples' labor supplies, investigating their similarity to heterosexual decision-making, in a collective household framework. Data from the 2000 US Census show that couples of all types exhibit a significant response to bargaining power shifts, as measured by differences between partners in age or non-labor income. In gay, lesbian, and heterosexual cohabiting couples, a relatively young or rich partner has more bargaining power and hence supplies less labor, the opposite holding for his/her mate. Married couples value the older spouse instead, or the richer. No effects are found for same-sex roommates

A Qualitative Exploration of the Use of Contraband Cell Phones in Secured Facilities

Offenders accepting contraband cell phones in secured facilities violate state corrections law, and the possession of these cell phones is a form of risk taking behavior. When offenders continue this risky behavior, it affects their decision making in other domains where they are challenging authorities; and may impact the length of their incarceration. This qualitative phenomenological study examined the lived experience of ex-offenders who had contraband cell phones in secured correctional facilities in order to better understand their reasons for taking risks with contraband cell phones. The theoretical foundation for this study was Trimpop\u27s risk-homeostasis and risk-motivation theories that suggest an individual\u27s behaviors adapt to negotiate between perceived risk and desired risk in order to achieve satisfaction. The research question explored beliefs and perceptions of ex-offenders who chose to accept the risk of using contraband cell phones during their time in secured facilities. Data were collected anonymously through recorded telephone interviews with 8 male adult ex-offenders and analyzed using thematic content analysis. Findings indicated participants felt empowered by possession of cell phones in prison, and it was an acceptable risk to stay connected to family out of concern for loved ones. The study contributes to social change by providing those justice system administrators, and prison managers responsible for prison cell phone policies with more detailed information about the motivations and perspectives of offenders in respect to using contraband cell phones while imprisoned in secured facilities

M_STATS: Stata module to implement interpoint distance distribution analysis

M_STATS contains two commands, mstat and mtest, to perform inference based on the M statistic, a statistic used to compare the interpoint distance distribution across groups of observations. The two commands can be used to test the null hypothesis that two groups have the same (spatial) distribution. mstat and mtest return the exact M test statistic. Moreover, mtest executes a Monte Carlo type permutation test, returning the empirical p-value together with its confidence interval.M statistic, spatial data, interpoint distance distribution

Interactive epistemology in simple dynamic games with a continuum of strategies

We extend the epistemic analysis of dynamic games of Battigalli and Siniscalchi (1999, 2002, 2007) from finite dynamic games to all simple games, that is, finite and infinite-horizon multistage games with finite action sets at non-terminal stages and compact action sets at terminal stages. We prove a generalization of Lubin's (1974) extension result to deal with conditional probability systems and strong belief. With this, we can provide a short proof of the following result: in every simple dynamic game, strong rationalizability characterizes the behavioral implications of rationality and common strong belief in rationality

M statistic commands: Interpoint distance distribution analysis

We implement the commands mstat and mtest to perform inference based on the M statistic, a statistic that can be used to compare the interpoint distance distribution across groups of observations. The analyses are based on the study of the interpoint distances between n points in a k-dimensional setting to produce a one-dimensional real-valued test statistic. The locations are distributed in a region of the plane. When we consider all interpoint distances, the dependencies among them are difficult to express analytically, but their distribution is informative, and the M statistic can be built to summarize one aspect of this information. The two commands can be used on a wide class of datasets to test the null hypothesis that two groups have the same (spatial) distribution. mstat and mtest return the exact M test statistic. Moreover, mtest executes a Monte Carlo–type permutation test, which returns the empirical p-value together with its confidence interval. This is the command to use in most situations, because the convergence of M to its asymptotic chi-squared distribution is slow. Both commands can be used to obtain graphical output of the empirical density function of the interpoint distance distributions in the two groups and the twodimensional map of the n observations in the plane. The descriptions of the commands are accompanied by examples of applications with real and simulated data. We run the test on the Alt and Vach grave site dataset (Manjourides and Pagano, forthcoming, Statistics in Medicine) and reject the null hypothesis, in contradiction to other published analyses. We also show how to adapt the techniques to discrete datasets with more than one unit in each location. Finally, we report an extensive application on breast cancer data in Massachusetts; in the application, we show the compatibility of the M commands with Pisati’s spmap package

Centrality measures in networks

International audienceWe show that prominent centrality measures in network analysis are all based on additively separable and linear treatments of statistics that capture a node’s position in the network. This enables us to provide a taxonomy of centrality measures that distills them to varying on two dimensions: (i) which information they make use of about nodes’ positions, and (ii) how that information is weighted as a function of distance from the node in question. The three sorts of information about nodes’ positions that are usually used—which we refer to as “nodal statistics”—are the paths from a given node to other nodes, the walks from a given node to other nodes, and the geodesics between other nodes that include a given node. Using such statistics on nodes’ positions, we also characterize the types of trees such that centrality measures all agree, and we also discuss the properties that identify some path-based centrality measures

M statistic commands: Interpoint distance distribution analysis

We implement the commands mstat and mtest to perform inference based on the M statistic, a statistic that can be used to compare the interpoint distance distribution across groups of observations. The analyses are based on the study of the interpoint distances between n points in a k-dimensional setting to produce a one-dimensional real-valued test statistic. The locations are distributed in a region of the plane. When we consider all (n 2) interpoint distances, the dependencies among them are difficult to express 2 analytically, but their distribution is informative, and the M statistic can be built to summarize one aspect of this information. The two commands can be used on a wide class of datasets to test the null hypothesis that two groups have the same (spatial) distribution. mstat and mtest return the exact M test statistic. Moreover, mtest executes a Monte Carlo–type permutation test, which returns the empirical p-value together with its confidence interval. This is the command to use in most situations, because the convergence of M to its asymptotic chi-squared distribution is slow. Both commands can be used to obtain graphical output of the empirical density function of the interpoint distance distributions in the two groups and the two- dimensional map of the n observations in the plane. The descriptions of the commands are accompanied by examples of applications with real and simulated data. We run the test on the Alt and Vach grave site dataset (Manjourides and Pagano, forthcoming, Statistics in Medicine) and reject the null hypothesis, in contradiction to other published analyses. We also show how to adapt the techniques to discrete datasets with more than one unit in each location. Finally, we report an extensive application on breast cancer data in Massachusetts; in the application, we show the compatibility of the M commands with Pisati's spmap package. Copyright 2011 by StataCorp LP.mstat, mtest, M statistic, interpoint distance, Monte Carlo test, spmap