Health care issues among lesbian, gay, bisexual, transgender and intersex (LGBTI) populations in the United States: Introduction

This supplemental issue of the Journal of Homosexuality presents research that explores a variety of health care issues encountered by lesbian, gay, bisexual, transgender and intersex (LGBTI) population groups in the United States over the 10-year period from 1993 to 2002. Topics include access to health care, utilization of care, training of medical and mental health providers, and the appropriate preparation of clinical offices and waiting areas. Authors used a variety of community-based public health research methods, including participant and provider surveys and retrospective chart reviews of patients, to develop this body of research, providing a recent-historical perspective on the complex health care and health-related needs of sexual and gender minorities. Particularly for transgender and intersex populations, the state of research describing their health care needs is in its infancy, and much remains to be done to design effective medical and mental health programs and interventions.     

The potential impact of discrimination fears of older gays, lesbians, bisexuals and transgender individuals living in small- to moderate-sized cities on long-term health care

As an exploration of the potential impact of fears of discrimination against GLBTs in long-term health care settings, this study compared perceptions of GLBT persons and heterosexuals. A total of 132 GLBT persons and 187 heterosexuals living in Eastern Washington completed a survey that contained demographic questions and perceptions of discrimination in long-term care settings. Most respondents suspected that staff and residents of care facilities discriminate against GLBTs. GLBT respondents who believed that residents of care facilities are victims of discrimination were more likely to believe that they would have to hide their sexual orientation if admitted to a care facility. GLBT respondents were more likely than heterosexual respondents to believe that GLBTs do not have equal access to health care and social services, that GLBTs residents of care facilities are victims of discrimination, that GLBT sensitivity training programs would benefit staff and residents of care facilities, and that GLBT retirement facilities would be a positive development for older GLBTs. This study is offered as a preliminary investigation of concerns about GLBT discrimination in health care settings, how concerns are expressed, and the implications of those concerns for health care needs.     

Ethnic Classification in Global Perspective: A Cross-National Survey of the 2000 Census Round

Academic interest in official systems of racial and ethnic classification has grown in recent years, but most research on such census categories has been limited to small case studies or regional surveys. In contrast, this article analyzes a uniquely global data set compiled by the United Nations Statistical Division to survey the approaches to ethnic enumeration taken in 141 countries. The motives for this analysis combine theoretical, applied, and policy objectives. I find that 63% of the national censuses studied incorporate some form of ethnic enumeration, but their question and answer formats vary along several dimensions that betray diverse conceptualizations of ethnicity (for example, as “race” or “nationality”). Moreover, these formats follow notably regional patterns. Nonetheless, the variety of approaches can be grouped into a basic taxonomy of ethnic classification approaches, suggesting greater commonality in worldwide manifestations of the ethnicity concept than some have recognized.

Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality

Spatially explicit data pose a series of opportunities and challenges for all the actors involved in providing data for long-term preservation and secondary analysis—the data producer, the data archive, and the data user. We report on opportunities and challenges for each of the three players, and then turn to a summary of current thinking about how best to prepare, archive, disseminate, and make use of social science data that have spatially explicit identification. The core issue that runs through the paper is the risk of the disclosure of the identity of respondents. If we know where they live, where they work, or where they own property, it is possible to find out who they are. Those involved in collecting, archiving, and using data need to be aware of the risks of disclosure and become familiar with best practices to avoid disclosures that will be harmful to respondents.

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Pooling designs associated with unitary space and ratio efficiency comparison

Let mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} be a (2ν+δ)-dimensional unitary space of mathbbF_q²mathbb{F}_{q^{2}} , where δ=0 or 1. In this paper we construct a family of inclusion matrices associated with subspaces of mathbbF^(2n+d)_q²mathbb{F}^{(2nu+delta)}_{q^{2}} , and exhibit its disjunct property. Moreover, we compare the ratio efficiency of this construction with others, and find it smaller under some conditions.     

Simple methods for evaluating and comparing binary experiments

We consider a confidence parametrization of binary information sources in terms of appropriate likelihood ratios. This parametrization is used for Bayesian belief updates and for the equivalent comparison of binary experiments. In contrast to the standard parametrization of a binary information source in terms of its specificity and its sensitivity, one of the two confidence parameters is sufficient for a Bayesian belief update conditional on a signal realization. We introduce a confidence-augmented receiver operating characteristic for comparisons of binary experiments for a class of “balanced” decision problems, relative to which the confidence order offers a higher resolution than Blackwell’s informativeness order.

Where observation is concerned, Chance favors only the prepared mind. —Louis Pasteur (1822–1895).

     

Choosing monetary sequences: theory and experimental evidence

We formulate and investigate experimentally a model of how individuals choose between time sequences of monetary outcomes. The model assumes that a decision maker uses, sequentially, two criteria to screen options. Each criterion only permits a decision between some pairs of options, while the other options are incomparable according to that criterion. When the first criterion is not decisive, the decision maker resorts to the second criterion to select an alternative. We find that: (1) traditional economic models based on discounting alone cannot explain a significant (almost 30%) proportion of the data no matter how much variability in the discount functions is allowed; (2) our model, despite considering only a specific (exponential) form of discounting, can explain the data much better solely thanks to the use of the secondary criterion; (3) our model explains certain specific patterns in the choices of the “irrational” people. We reject the hypothesis that anomalous behavior is due simply to random “mistakes” around the basic predictions of discounting theories: deviations are not random and there are clear systematic patterns of association between “irrational” choices.     

Population monotonic path schemes for simple games

A path scheme for a game is composed of a path, i.e., a sequence of coalitions that is formed during the coalition formation process and a scheme, i.e., a payoff vector for each coalition in the path. A path scheme is called population monotonic if a player’s payoff does not decrease as the path coalition grows. In this study, we focus on Shapley path schemes of simple games in which for every path coalition the Shapley value of the associated subgame provides the allocation at hand. Obviously, each Shapley path scheme of a game is population monotonic if and only if the Shapley allocation scheme of the game is population monotonic in the sense of Sprumont (Games Econ Behav 2:378–394, 1990). We prove that a simple game allows for population monotonic Shapley path schemes if and only if the game is balanced. Moreover, the Shapley path scheme of a specific path is population monotonic if and only if the first winning coalition that is formed along the path contains every minimal winning coalition. We also show that each Shapley path scheme of a simple game is population monotonic if and only if the set of veto players of the game is a winning coalition. Extensions of these results to other efficient probabilistic values are discussed.