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The association between multiple domains of discrimination and self-assessed health: a multilevel analysis of Latinos and blacks in four low-income New York city neighborhoods.
Racial disparities in health have been shown to persist even after adjusting for socioeconomic status (Krieger et al. 1993; Williams and Collins 1995; Lille-Blanton and LaVeist 1996; Navarro 1990). This observation has resulted in a search for other explanations for the racial differences in health in the United States. A growing body of research is investigating the potential role of discrimination in shaping racial differences in health. Research has shown that individual experiences of racial discrimination are linked to depression (Salgado de Snyder 1987) and psychological distress (Ladrine et al. 1995; Meyer 1995; Broman 1996; Jackson et al. 1996; Amaro, Russo, and Johnson 1987; Williams et al. 1997). Experiences of discrimination have also been shown to be associated with rates of hypertension (Krieger 1990), raised blood pressure (James et al. 1984; Krieger and Sidney 1996), poorer self-rated health (Williams et al. 1997), increased cigarette smoking (Ladrine and Klonoff 1996), more reported days spent unwell in bed (Williams et al. 1997), and low birth weight among children whose mothers have been discriminated against (Collins et al. 2000).
The purpose of this study was to examine the association between individual experiences of discrimination and self-assessed mental and physical health among Latinos and blacks while adjusting for other known determinants of health (such as socioeconomic status and access to health care) that may be important confounders or mediators of this relationship. We also assessed if multiple domains of discrimination (racial discrimination and discrimination due to other attributes such as sex, age, sexual orientation, etc.) were associated with poor health.
Discrimination has been defined as the "process by which a member, or members, of a socially defined group is, or are, treated differently because of his/her/their membership of that group" (Jary and Jary 1995). Discrimination may exist in multiple forms. For example, individual experiences of discrimination refer to discriminatory interactions between individuals that can be directly perceived. Structural discrimination refers to policies or practices that are discriminatory (Krieger 2000). Residential segregation is one form of structural discrimination that may affect health; other structural forms of structural discrimination such as redlining and political empowerment have also been studied (Gee 2002; Bobo and Gilliam 1990).
It has been suggested that individual experiences of racial discrimination may generate stress and in turn alter physiological processes that adversely affect health (Williams et al. 1997). However, it has been shown that individuals exposed to the same experience of discrimination have different stress responses. Research has found that blood pressure may be highest among blacks who actively try to overcome adversities (such as racial discrimination) but who have limited educational and socioeconomic resources to do so (James et al. 1984, 1987; James 1994).
The association between experiences of racial discrimination and health may also be affected by experiences of discrimination due to other attributes. While racial discrimination is the most prevalent form of discrimination experienced in the United States (Kessler, Mickelson, and Williams 1999), discrimination due to attributes such as one's sexual orientation, gender, age and mental illness are also common (Essed 1992; Vaid 1995; Gill 1996). A national survey of U.S. residents revealed that 33 percent of minority and nonminority respondents reported experiencing a major event of discrimination in their lifetime due to one of several attributes (Kessler, Mickelson, and Williams 1999). Persons who have more than one attribute that can be cause for discrimination may suffer discrimination due to multiple attributes. In one study, white gay men reported mainly antigay discrimination while lesbian women reported both antigay and gender discrimination, and black, gay women reported racial discrimination, antigay discrimination, and gender discrimination (Krieger and Sidney 1997). Another study found that lesbian and gay blacks reported higher rates of psychological distress than would be predicted based on the sum of their risk from experiences of racial, gender, and sexual orientation discrimination (Cochran and Mays 1994; Krieger 2000).
To appreciate the unique association between discrimination and health it is important to control for other potentially relevant contextual and individual-level factors that may confound or mediate this relationship. One such contextual factor is the racial and ethnic composition of one's neighborhood. Although research on the potential impact of racial/ethnic composition is sparse, evidence from research on residential segregation and health suggests some potential associations. Residential segregation may shape access to educational and employment opportunities and hence residents' socioeconomic status. According to this argument, individual socioeconomic status mediates the direct relationship between residential segregation and health (Williams 1999). Health can then be shaped by individual-level mechanisms such as health behaviors, which are associated with lowered socioeconomic status. For example it has been shown that low socioeconomic status, concentrated in areas of residential segregation, is associated with higher smoking rates (Hint and Novotny 1997). Racially segregated neighborhoods also have a paucity of facilities conducive to healthy lifestyles (such as athletic tracks or playing fields) (Williams and Collins 2001). In addition, targeted advertising for tobacco and alcohol in neighborhoods with a particular racial/ethnic composition, (Krieger 2000; Williams and Collins 2001) may impact health-related behaviors.
The racial and ethnic composition of one's neighborhood may also affect health by influencing more proximal determinants of health. For example, segregated communities frequently face a lack of health care providers and disproportionately low rates of health insurance; both factors are important predictors of differential access to medical care (Mayberry, Mill, and Ofili 2000). It has been shown that racial and ethnic minorities are less likely than whites to possess health insurance and to have a regular health care provider. They are more likely than whites to have difficulty getting care, to have fewer choices of where to receive care, and to receive care in an emergency room (Collins, Hall, and Neuhaus 1999).
Social relations and social support may play a unique role in shaping the relation between discrimination and poor health. For example, individuals with stronger social networks and higher levels of perceived social support may be better able to cope with major life stressors (Hirsch and Dubois 1992; Rhodes et al. 1994). By contrast, those who report lower levels of social support have been shown to be associated with increased risk of dying prematurely from several causes of death (see, for example, Berkman 1995; Berkman and Kawachi 2000). Resilience and vulnerability related to an individual's social support may affect her or his ability to cope with the stresses of discrimination and may ultimately affect the relationship between discrimination and health.
In this study we explored the association between discrimination and health in both blacks and Latinos. It is important to explore the association between discrimination and health for other minority groups besides blacks because while blacks report the highest prevalence of discrimination experiences in studies among all racial/ethnic groups; other minority groups, namely Latinos and Asian-Pacific Islanders, also report discrimination experiences at higher levels than whites (Gee 2002; Public Health Special 2001). Although prior research suggests that multiple domains of discrimination experienced by individuals may be associated with poor health (Cochran and Mays 1994; Krieger and Sidney 1997; Diaz et al. 2001), little is known about the relation between experiences of multiple domains of discrimination and health. We assessed if individuals who experienced racial discrimination in addition to discrimination due to other attributes had worse health outcomes than those who experienced only a single domain of discrimination, racial or otherwise. The conceptual framework outlined above highlights the complexity of relationships that may exist between different social determinants and health. While prior work acknowledges the need to disentangle the relations between social and individual factors and health disparities, few studies consider the association between discrimination and health while adjusting for the presence of multiple potentially confounding or mediating factors. In this study, we assessed the relation between individual and contextual forms of discrimination and health while controlling for socioeconomic status, access to health care, social support, and smoking.
In this cross-sectional study, our sampling frame was adults 18 years of age or older living in the South Bronx, East Harlem, Central Harlem, and Bedford-Stuyvesant New York City, in households with telephones, during 2002. Neighborhoods were selected and defined by zip codes, which were determined through consultation with community members to be a reasonable approximation of the neighborhoods in these areas. Using random-digit dialing, calls to households were made during evenings, weekends, and normal working hours. Numbers where there was no one at home received repeated calls daily for up to ten days. Once a household was reached, geographic eligibility was determined by asking respondents to identify their zip code. Households within our neighborhood boundaries were considered eligible.
We made 10,187 telephone contacts. Among these, 827 were never answered except by answering machines, and 393 numbers were not eligible for other reasons (mainly languages other than English and Spanish). We spoke with a total of 8,967 households; 2,459 were callbacks still not reached at the end of the study to complete the screening for eligibility. Among the 6,508 households with a resolved contact, 2,012 refused to complete the initial screening for the interviewing. Among the 4,496 screened, 3,072 persons screened out of the survey, 206 were not interviewed because the quota for their gender and neighborhood had been filled, and we completed interviews with 1,003 of the remaining 1,218 persons. After qualifying, 121 refused, and 94 were in callback status at study completion. The overall cooperation rate based on the sum of the number of completed interviews, quota outs, and screen-outs (i.e., 1,003 +206 +3,072) divided by the sum of completed interviews, quota outs, screen outs, refusals, and premature terminations (i.e., 1,003 +206 +3,072 +2,012 +121) was 66.7 percent. The overall response rates was 43.7 percent based on number of completed interviews, quota outs, and screen-outs divided by the sum of all contacts, persons not screened, refusals, and call backs not screened (American Association for Public Opinion Research 2000).
For households with more than one eligible adult, a respondent was randomly selected using a variation of the Kish procedure (Kish 1949). We asked to interview the household member whose birthday fell closest to the date of our call. This method ensured that the selected respondent was not simply the one who happened to be at home the most or who was most interested in talking on the phone. All responses were weighted to account for the number of individuals within a household and number of telephones within the household. All analyses were carried out using the weighted sample. The study was reviewed and approved by the Institutional Review Board of The New York Academy of Medicine.
A structured 25-minute interview was administered by trained interviewers using a computer-assisted telephone interviewing system. The instrument was available in English and Spanish. Back translation of the instrument from Spanish to English verified the consistency of questions asked. Native Spanish and English speakers carried out the interviews in their respective languages. The interview included questions regarding each respondent's age, gender, educational level, marital status, physical and mental health, family income, social support, access to health insurance and a regular source of care, smoking behavior, and experiences of discrimination. The survey instrument was pilot-tested in telephone interviews before the start of the study.
1. Self-assessed Physical and Mental Health. We used two of the Centers for Disease Control's Health Related Quality of Life Measures to assess physical and mental health. These questions have known validity, reliability, and responsiveness (Andreson et al. 2000, 2001) and have been used in other well-known questionnaires (Behavioral Risk Factor Surveillance System Questionnaire and the National Health and Nutrition Examination Survey). The questions are: "for how many days during the past 30 days was your physical health not good?" and "for how many days during the past 30 days was your mental health not good?" We created two variations of both the physical and mental health measure: an ordered polychotomous dependent variable coded as no poor health days, poor health days below the mean for the sample, and poor health days above the mean for the sample and a dichotomous outcome variable coded as no poor health days and at least one poor health day.
2. Sociodemographic Measures. Age was coded as a categorical variable (18-34, 35-54, 55-64, 65 or older). Race was dummy-coded (black and Latino). Country of origin was also dummy-coded among Latino respondents (Puerto Rico, Dominican Republic, Mexico, and Other). Information on income and education was obtained to characterize the socioeconomic status of respondents. Education or schooling was coded as either less than high school education, high school education, or greater than a high school education. Household income was measured in the last year and was dummy-coded in three categories (less than $20,000, between $20,000 and 50,000, and more than $50,000). A dummy variable was also created for persons who were missing data on income.
3. Interpersonal Discrimination. To record perceptions of discrimination we asked the following question: "Have you ever been discriminated against, including being hassled or prevented from doing something because of any of the following [ITEM]?" (Krieger 1990). The question was repeated for each of the following variables: age, race, gender, appearance such as obesity or tattoos, poverty, being on welfare, religion, mental illness, physical illness or disability, immigration status, sexual orientation, and having a criminal record. Respondents were prompted to provide other answers and these were recorded if volunteered. We dummy-coded responses to this question in the following manner: 0 = no discrimination reported, 1 = only racial discrimination reported, 2 = nonracial discrimination reported, 3 = racial and nonracial discrimination reported.
4. Other Relevant Covariates. We determined individual social support using three questions from the Medical Outcomes Study: "In the last 12 months, how often was each of the following available to you [ITEM]?" The items were: someone available to help you if you were confined to a bed, someone available to give you good advice about a crisis, someone available to love you and make you feel wanted (Sherbourne and Stewart 1991). These items have an alpha correlation of .76 in previous work done by the authors (Galea et al. 2002). Possible responses to these questions included none of the time, some of the time, most of the time, or all of the time. Responses were summed to create a scale ranging from 1 to 12. Those receiving a score between 9 and 12 (indicating a high level of social support) were coded as a 0, those receiving a score between 5 and 8 (indicating a medium level of social support) were coded as a 1, and those receiving a score of less than or equal to 4 were coded as a 2 indicating low levels of social support.
Separate black and Latino measures of the racial and ethnic composition of the neighborhood were created and analyzed in separate models. These measures were based on the proportion of black and Latino residents within the study's zip codes compiled from 2000 Census data. We created a categorical variable to compare areas of low racial and ethnic composition (proportion of black or Latino residents is less than 25 percent) to areas of medium racial and ethnic composition (proportion of black or Latino residents is between 25 and 50 percent) and high areas of racial and ethnic composition (the proportion of black and Latino residents is greater than 50 percent).
To assess access to health care we asked respondents if they have health insurance and a regular source of care. Having health insurance coverage was modeled as a dichotomous variable where not having health insurance was coded a 1 and having insurance was coded as 0. Having a usual source of care was also included as a dichotomous variable where not having a usual source of care was coded as 1 and having a usual source of care was coded as 0. If the respondent reported smoking cigarettes in the past 12 months they were coded as a 2 for everyday smoker, 1 for some day smoker, and 0 for nonsmoker.
Statistical Analysis
Nonblack and non-Latino respondents were excluded for a total weighted sample of 873. We provide descriptive statistics for the study population and used a chi-square test to assess differences between blacks and Latinos. We show bivariate relations between the covariates of interest and self-assessed mental and physical health. Using the full sample, we used Generalized Estimating Equations (GEE) to fit multilevel multivariable models to assess the relationship between the covariates of interest and self-assessed mental and physical health controlling for potential intrazip code correlations. Racial and ethnic composition was modeled as a zip-code-level variable in all multivariable models. We modeled the dependent variables of interest (poor mental and physical health days) as both polychotomous and dichotomous variables. Model fit was not appreciably different and there was little substantive difference in effect parameters in both forms. Here we show only results of the logistic models. Parameter estimates and standard errors are shown for unadjusted and adjusted analysis. Statistical interaction between key individual and group-level determinants was examined using model interaction terms. To facilitate a discussion of the magnitude of our results, we calculated model-based relative changes in probability of poor mental and physical health based upon various combinations of racial and other domains of discrimination. To assess differences in association between discrimination and mental and physical health in blacks and Latinos we carried out stratified analyses by race.
Overall sample sociodemographics were comparable to census sociodemographics for the total sample within each of the four neighborhoods (data not shown). Table 1 shows selected respondent characteristics for the overall sample and stratified by race. Latinos compared with black respondents had lower reported household incomes, less education, and were younger. Latinos were more likely to report no experience of discrimination in their lifetime (62 percent) compared with black respondents (47 percent). Notably, black respondents reported experiences of racial discrimination (17 percent) more often than Latino respondents (8 percent). Blacks also were more likely than Latinos to report multiple domains of discrimination (23 percent versus 18 percent). Latinos reported being uninsured more often than blacks (17 percent versus 9 percent), and were less likely to report high levels of perceived social support compared to blacks (20 percent versus 33 percent). Blacks reported smoking every day more often than Latinos (20 percent versus 12 percent).
Unadjusted and adjusted associations between each covariate and poor mental health are shown in Table 2. The following factors were positively associated with poor mental health in adjusted analyses: younger age, any experience of discrimination (racial, nonracial, and multiple domains), and living in a highly segregated Latino neighborhood. By contrast, respondents living in highly segregated black neighborhoods were less likely to report poor mental health.
Shown in Table 3, factors that were positively associated with poor physical health in adjusted analyses were having less than a high school education and living in a highly segregated black neighborhood. Respondents living in highly segregated Latino neighborhoods were less likely to report physical health problems. Interpersonal experiences of discrimination were not associated with poor physical health.
To facilitate a discussion of the magnitude of the relation between interpersonal discrimination and poor mental and physical health we calculated model-based relative probability changes by varying the domains of discrimination under specified model conditions (see Legend to Table 4). Although these results are not generalizable to each member of the sample, they provide an estimate of the magnitude of the relation between experiences of discrimination and health at the population level. Respondents experiencing only racial discrimination were 17 percent more likely to report poor mental health compared with respondents experiencing no discrimination. Respondents experiencing a domain of discrimination other than race were 34 percent more likely to report poor mental health compared with respondents reporting no discrimination. Respondents experiencing racial discrimination and another domain of discrimination were 23 percent more likely to report poor mental health compared with respondents reporting no discrimination. By contrast, the association of interpersonal discrimination and poor physical health was smaller (8-9 percent).
Adjusted analyses stratified by race (Table 5) showed that experiences of discrimination were more highly associated with poor mental health among blacks as compared with Latinos, although the effect sizes for the parameter estimate measuring discrimination were not directly comparable.
Identifying the factors strongly associated with poor health in minority populations is an essential first step toward the elimination of racial disparities in health. In this study, we showed that in four low-income neighborhoods in New York City experiences of racial and other domains of discrimination were common among blacks and Latinos and were comparable with estimates observed in the general population (Kessler, Mickelson, and Williams 1999). Blacks were more likely than Latinos to report experiences of both racial and other domains of discrimination. We showed that after adjusting for contextual and individual factors identified by the previous literature as plausible explanations for racial health disparities, experiences of discrimination, including discrimination due to race, due to other domains, and due to multiple domains were associated with poor mental health. Although not always in the ways expected, there was a relationship between the racial and ethnic composition of neighborhoods and poor mental and physical health. High racial and ethnic composition was associated with poor physical health among blacks and with poor mental health among Latinos. By contrast, individuals living in disproportionately black neighborhoods were less likely to report poor mental health and individuals living in highly segregated Latino zip codes were less likely to report poor physical health.
The finding that experiences of discrimination were associated poor mental but not physical health is consistent with other research (Krieger 2000; Gee 2002; Williams, Spencer, and Jackson 1999). We used a measure of mental health that may have encompassed several aspects of mental health, including life satisfaction, feelings of depression, unhappiness, and psychological distress, and we measured the role of perceived discrimination as a recent stressor. These aspects of mental health were plausibly influenced by situational stressors in the relative short-term and thus may have been more susceptible to our discrimination measure (Benschop and Schedlowski 1999). However, the measure of physical health we used in this study may reflect more chronic conditions with complicated intervening and mediating factors that might modify the hypothesized relations between discrimination and physical health. Longitudinal research that takes into account the cumulative burden of discrimination over time is needed to further explore the relationship between experiences of discrimination and health. One such study found that cumulative experiences of discrimination resulted in poorer mental health but, contrary to the authors' expectations, was also associated with slightly better physical health (Jackson et al. 1996).
In our study, there was no independent relation between race and mental health in multivariable models adjusting for discrimination and other covariates. In stratified analyses, the association between discrimination and poor mental health was somewhat greater for blacks as compared with Latinos, although model coefficients between stratified black and Latino models were not directly comparable. There has been relatively little research on the relationship between discrimination and health and this is especially true for nonblack minorities. One study examining a broad array of discrimination experiences showed an association between poor mental health and discrimination only among blacks but not other minority groups (Williams, Spencer, and Jackson 1999). A study of Mexican-American women found that racial discrimination was associated with higher levels of psychological distress in this group (Salgado de Snyder 1987). Gee (2002) showed that racial discrimination adversely affects the health of Chinese Americans. Our work begins to suggest that the relative contribution of discrimination to health may differ between racial/ethnic groups and the risk factors that confound or modify these relations may vary by race and ethnicity.
In this study Latinos were less likely than blacks to have access to health insurance and to perceive high levels of social support. If these factors mediate the adverse relation between discrimination and health, experiences of discrimination may be more detrimental to health for Latinos than for blacks. For example, the existence of social support may allow a person experiencing discrimination to recognize and discuss these experiences with others. On the other hand, experiences of racial discrimination may be more common for blacks as compared with Latinos, suggesting that at a population level, the influence of discrimination on health may be more influential in explaining higher rates of adverse health conditions among blacks than among other racial/ethnic groups.
Also of note was the substantial association between nonracial forms of discrimination and mental health. Our study was not adequately powered to examine the association between each domain of discrimination measured in this study and the dependent variables of interest. Because of the uniqueness of our black and Latino sample, we focused on the impact of race discrimination and grouped other domains of discrimination together. Ideally we would have assessed the association of each domain of discrimination separately. Although discrimination due to race appears to be the most prevalent form of discrimination in the general population (Kessler, Mickelson, and Williams 1999), a growing body of research has shown that multiple types of discrimination affect health differently in specific subgroups (Krieger 1990; Landrine et al. 1995; Mays and Cochran 2001).
Persons of color disproportionately have attributes that make them susceptible to experiencing multiple domains of discrimination compared with whites. For example, ethnic minorities are overrepresented among the poor. The poor are disproportionately women who live in single-parent households and are at increased risk for a broad spectrum of mental health disorders. In this study, we found that persons who experienced more than one domain of discrimination were more likely to be in poor mental health. This suggests that in measuring discrimination due to categorical domains we may be underestimating the role of discrimination as a potential explanation for racial disparities in health. The ultimate consequences of discrimination are likely to include interactions between multiple domains of interpersonal discrimination. Research that is powered to detect statistical interaction is needed to assess the role of multiple domains of discrimination on health.
The finding that residents of neighborhoods where there was a high proportion of blacks were more likely to have poor physical health, but not poor mental health, was somewhat puzzling. Also unexpected was our observation that residents of neighborhoods where there was a high proportion of Latinos were more likely to have poor mental health but were less likely to be in poor physical health. In our conceptual framework we discussed possible mechanisms whereby the racial and ethnic composition of neighborhoods may harm health, although research in this area is in its infancy. It is possible that having a high composition of one ethnic group in a neighborhood would positively affect mental health by increasing mutual social support and by reducing exposure to discrimination (Halpern 1993; Halpern and Nazroo 1999).
In addition, there is some recent evidence to suggest that blacks and Latinos may have different reporting patterns of self-assessed physical and mental health. Two recent studies found that race and ethnicity were associated with disparities in self-assessed health status independent of socioeconomic status, suggesting there may be racial and cultural variation in the interpretation of health (Ren and Amick 1996; Zimmer et al. 2000).
Telephone surveys present enormous challenges. In our study we had a response rate of 43.7 percent and a cooperation rate of 66.7 percent despite efforts made to minimize refusal rates and to recontact individuals. While approximately 96 percent of New York residents have telephones in their homes, this percentage may not be as high in lower-income neighborhoods (Northridge et al. 1998). In addition, individuals without working telephones may be different from other residents of the city in ways that might obscure the observed association between discrimination and health. For example, prior research conducted in Harlem has shown that nearly one-fifth of the population lacked working telephones, and persons in those households had higher rates of smoking (61 percent) than those living in homes with working telephones (40 percent) (Northridge et al. 1998). Persons without phones may be more vulnerable to experiences of discrimination because of their lower socioeconomic status. It is also plausible that persons who have been discriminated against in the past may be less likely to respond to telephone surveys because of previous negative experiences and a lack of trust toward an unknown, unseen interviewer. However, we note that careful comparisons between our sample and census demographics found no significant differences between our respondents and residents of the four neighborhoods surveyed. It is reassuring that we have obtained a representative sample of residents in these neighborhoods. Insofar as it is plausible that persons who may have been discriminated against may be less likely to participate in this study, this would result in an underestimation of the prevalence of discrimination and its association to health.
For this study the populations of the South Bronx, East Harlem, Central Harlem, and Bedford-Stuyvesant in New York City were sampled. The racial and ethnic composition of each neighborhood was classified based on the respondent's zip code. Zip codes may not be homogeneous in their sociodemographic characteristics (Kaplan and Van Valey 1980). Although it would have been optimal to obtain the respondents' home addresses so that we could link the survey data to census tracts and block-groups, which are based on smaller numbers of people, this was not possible in the context of an anonymous survey that was designed to ask about sensitive behaviors and experiences. We note that in the context of the neighborhoods being studied, zip codes were probably a reasonable approximation of meaningful neighborhoods for their residents and, as such, we feel that, within stated limitations, provide valuable data that can form the empiric basis for the observations in this manuscript.
We used self-reported measures of discrimination, physical and mental health, and the other relevant covariates. Although self-reported health has been shown to be a robust predictor of morbidity and mortality (Idler and Angel 1990; Ferraro and Su 2000), it is possible that persons who are more likely to perceive discrimination may also overreport mental or physical health problems. In addition, there is ongoing recognition that the cumulative burden of discrimination, individual responses to it, and the context in which discrimination occurs (e.g., employment, from police) mediate or modify discrimination's relation to health. Research is needed to assess the relative importance of these factors for different domains of discrimination in varying subgroups. Additional research is also needed to assess the health impacts of nonracial domains of discrimination. In this study nonracial domains of discrimination were more strongly correlated with poor mental health than discrimination due to race.
Because this study was carried out cross-sectionally we cannot infer causality in terms of the direction of the observed association between interpersonal discrimination and health. Although our study was adequately powered to detect relations between experiences of discrimination and the dependent variables of interest, a larger sample is needed to confirm the absence of cross-level interaction between experiences of discrimination and contextual discrimination observed in this study.
This study showed that experiences of discrimination were associated with poor mental health even when individual and contextual factors, which are commonly assumed to contribute to health disparities, were taken into account. These findings underscore the observation that socioeconomic status is only part of the explanation for observed racial disparities in health and investigation of further explanations is needed. In this analysis we showed that individuals experiencing multiple domains of discrimination were at a greater risk for poor mental health. Research on health disparities in the United States should include work on the association between racial and nonracial domains of discrimination and health and discrimination due to multiple attributes and health.
Table 1: Characteristics of Survey Respondents in Four New York City Neighborhoods (a) by Race Total, N = 873 Black, N = 455 (52%) (b) Demographics N % N % Gender Female 488 44.1 256 56.2 Male 385 56.0 199 43.8 Family income, $ <20,000 292 46.3 127 39.6 20,000-50,000 238 37.8 127 39.7 >50,000 100 15.9 66 20.7 Missing income 242 27.7 135 29.7 Age 18-34 343 40.4 154 35.1 35-54 329 38.8 169 38.4 55-65 95 11.2 61 13.8 65+ 81 9.6 56 12.7 Education Less than high school 210 24.6 68 15.4 High school graduate 262 30.7 142 32.2 Any college 382 44.7 232 52.4 Country of Origin Puerto Rica Dominican Republic Mexico Other Discrimination Types of discrimination No discrimination 474 54.3 216 46.5 Racial only 110 12.6 79 17.3 Nonracial only 180 20.6 56 12.3 Racial and nonracial 109 12.5 104 22.8 Other Relevant Covariates Insurance No 111 13.1 41 9.4 Yes 734 86.9 395 90.6 Regular source of care No 155 17.8 72 16.0 Yes 716 82.2 380 84.0 Social support Low 245 28.1 100 21.9 Medium 393 45.0 204 44.9 High 235 26.9 151 33.2 Smoke Every day 144 16.5 90 19.7 Some days 133 15.3 61 13.5 Not at all 595 68.2 304 66.8 Latino, N = 418 (48%) (c) ([X.sup.2]) Demographics N % (d) Gender 0.02 Female 233 55.7 Male 185 44.3 Family income, $ 18.34 * <20000 166 53.3 20,000-50,000 111 35.8 >50,000 34 11.0 Missing income 107 25.6 Age 20.97 ** 18-34 189 46.1 35-54 161 39.2 55-65 35 8.4 65+ 26 6.3 Education 43.76 ** Less than high school 142 34.4 High school graduate 120 29.1 Any college 150 36.5 Country of Origin Puerto Rica 209 50.4 Dominican Republic 88 21.1 Mexico 32 7.7 Other 86 20.8 Discrimination Types of discrimination 27.17 ** No discrimination 258 61.7 Racial only 31 7.5 Nonracial only 53 12.6 Racial and nonracial 76 18.2 Other Relevant Covariates Insurance 10.86 * No 70 17.0 Yes 340 83.0 Regular source of care 2.25 No 83 19.8 Yes 335 80.2 Social support 26.72 ** Low 145 34.8 Medium 189 45.2 High 84 20.1 Smoke 8.47 * Every day -54 12.2 Some days 72 17.3 Not at all 291 69.8 (a) The four neighborhoods included the South Bronx, East Harlem, Central Harlem, and Bedford-Stuyvesant (b) N may not add to 455 due to missing values. (c) N may not add to 418 due to missing values. (d) Two-tailed chi-square p-value. ** p < .001, * p < .05. Table 2: Unadjusted and Adjusted Associations between Covariates and Self-assessed Poor Mental Health (N= 843) (a) Poor Mental Health (b) Unadjusted Adjusted (c) b SE b SE Demographics Race Black -0.05 0.16 0.31 0.19 Hispanic (ref) -- -- -- -- Gender Female 0.13 0.16 0.28 0.17 Male (ref) -- -- -- -- Family income, $ >20,000 0.21 0.27 -0.02 0.31 20,000-50,000 -0.04 0.27 -0.24 0.30 > 50,000 (ref) -- -- -- -- Missing income -0.14 0.28 -0.2 0.31 Age 18-34 (ref) -- -- -- -- 35-54 -0.19 0.18 -0.25 0.19 55+ -0.77 * 0.30 -0.83 * 0.31 65+ -0.93 ** 0.28 -1.08 ** 0.30 Education Less than high school 0.28 0.20 0.64 * 0.26 High school graduate 0.26 0.19 0.43 * 0.21 Any college (ref) -- -- -- -- Discrimination Variables Types of discrimination No discrimination (ref) -- -- -- -- Racial only 0.22 0.23 0.53 * 0.25 Nonracial only 0.18 ** 0.23 1.24 ** 0.20 Racial and nonracial 0.74 ** 0.24 0.75 ** 0.26 Other Relevant Covariales Insurance No -0.008 0.25 -0.16 0.27 Yes (ref) -- -- -- -- Regular source of care No 0.23 0.21 0.13 0.24 Yes (ref) -- -- -- -- Social support Low 0.25 0.22 0.12 0.24 Medium 0.29 0.19 0.36 0.20 High (ref) -- -- -- -- Black composition <25% (ref) -- -- -- -- 25-500% -0.34 0.23 -0.31 0.24 50+% -0.59 ** 0.22 -0.60 * 0.23 Latino composition <25% (ref) -- -- 25-50% 0.36 0.22 50+% 0.42 * 0.18 Poor Mental Health (b) Adjusted (d) b SE Demographics Race Black 0.33 0.19 Hispanic (ref) -- -- Gender Female 0.28 0.17 Male (ref) -- -- Family income, $ >20,000 0.003 0.31 20,000-50,000 -0.23 0.30 > 50,000 (ref) -- -- Missing income -0.17 0.31 Age 18-34 (ref) -- -- 35-54 -0.24 0.19 55+ -0.86 * 0.30 65+ -1.06 ** 0.30 Education Less than high school 0.65 * 0.26 High school graduate 0.43 * 0.21 Any college (ref) -- -- Discrimination Variables Types of discrimination No discrimination (ref) -- -- Racial only 0.54 * 0.25 Nonracial only 1.26 ** 0.25 Racial and nonracial 0.74 ** 0.26 Other Relevant Covariales Insurance No -0.15 0.27 Yes (ref) -- -- Regular source of care No 0.13 0.24 Yes (ref) -- -- Social support Low 0.16 0.24 Medium 0.38 0.20 High (ref) -- -- Black composition <25% (ref) 25-500% 50+% Latino composition <25% (ref) -- -- 25-50% 0.40 0.24 50+% 0.49 * 0.21 (a) Sample size was reduced by 30 respondents due to missing values. (b) Poor mental health was defined as one or more poor mental health day in the past 30 days. (c) Model includes black neighborhood composition variable. (d) Model includes Latino neighborhood composition variable. ** p < .001, * p < .05. Table 3: Unadjusted and Adjusted Associations between Covariates and Self-assessed Poor Mental Health (N = 843) (a) Poor Physical Health (b) Unadjusted Adjusted (c) b SE b SE Demographics Race Black 0.16 0.16 0.15 0.19 Hispanic (ref) -- -- -- -- Gender Female 0.02 0.16 -0.07 0.17 Male (ref) -- -- -- -- Family income, $ >20,000 0.53 * 0.27 0.44 0.30 20,000-50,000 -0.09 0.28 -0.10 0.28 > 50,000 (ref) -- -- -- -- Missing income 0.15 0.29 -0.04 0.30 Age 18-34 (ref) -- -- -- -- 35-54 0.09 0.18 0.04 0.19 55+ -0.09 0.28 -0.21 0.30 65+ 0.46 0.26 0.33 0.27 Education Less than high school 0.56 * 0.20 0.57 * 0.23 High school graduate 0.14 0.19 0.05 0.21 Any college (ref) -- -- -- -- Discrimination Variables Types of discrimination No discrimination (ref) -- -- -- -- Racial only -0.10 0.67 0.20 0.25 Nonracial only 0.03 0.88 0.19 0.23 Racial and nonracial -0.01 0.96 0.17 0.24 Other Relevant Covariales Insurance No 0.41 0.26 0.42 0.29 Yes (ref) -- -- -- -- Regular source of care No 0.27 0.22 0.11 0.25 Yes (ref) -- -- -- -- Social support Low -0.007 0.22 -0.03 0.23 Medium -0.01 0.19 0.01 0.19 High (ref) -- -- -- -- Smoke Every day 0.05 0.21 -0.01 0.22 Some days 0.04 0.25 -0.04 0.26 Not at all (ref) -- -- -- -- Black composition <25% (ref) -- -- -- -- 25-500% 0.04 0.25 0.09 0.25 50+% 0.36 0.24 0.49 * 0.25 Latino composition <25% (ref) -- -- 25-50% -0.11 0.23 50+% -0.35 * 0.18 Adjusted (d) b SE Demographics Race Black 0.15 0.19 Hispanic (ref) -- -- Gender Female -0.08 0.17 Male (ref) -- -- Family income, $ >20,000 0.43 0.30 20,000-50,000 -0.10 0.28 > 50,000 (ref) -- -- Missing income -0.05 0.30 Age 18-34 (ref) -- -- 35-54 0.03 0.19 55+ -0.22 0.30 65+ 0.31 0.27 Education Less than high school 0.58 * 0.23 High school graduate 0.05 0.21 Any college (ref) -- -- Discrimination Variables Types of discrimination No discrimination (ref) -- -- Racial only 0.20 0.25 Nonracial only 0.17 0.23 Racial and nonracial 0.16 0.24 Other Relevant Covariales Insurance No 0.41 0.29 Yes (ref) -- -- Regular source of care No 0.09 0.25 Yes (ref) -- -- Social support Low -0.05 0.23 Medium 0.007 0.19 High (ref) -- -- Smoke Every day -0.003 0.22 Some days -0.04 0.26 Not at all (ref) -- -- Black composition <25% (ref) 25-500% 50+% Latino composition <25% (ref) -- -- 25-50% -0.04 0.25 50+% -0.41 * 0.29 (a) Sample size was reduced by 30 respondents due to missing values. (b) Poor mental health was defined as one or more poor mental health day in the past 30 days. (c) Model includes black neighborhood composition variable. (d) Model includes Latino neighborhood composition variable. ** p < .001, * p < .05. Table 4: Model-Based Relative Changes in Probability of Poor Health with Varying Domains of Discrimination (N= 843) (a) Discrimination Racial Nonracial Discrimination Discrimination Racial and Nonracial Only Only Discrimination No No No Yes No No No Yes No No No Yes % Change (d) Poor Poor Mental Physical Health (b) Health (c) -- -- 0.17 0.09 0.34 0.09 0.23 0.08 (a) Sample size was reduced by 30 respondents due to missing values. (b) Model assumes respondent was a middle-aged black female, with income of less than $20,000 a year, who has less than a high school education, insurance but no regular source of care, a medium level of social support, and who lived in a neighborhood that was 25-50% African American. (c) Model assumes respondent was a middle-aged black female, with an income of less than $20,000 a year, who has less than a high school education, has insurance but no regular source of care, has a medium level of social support, was an occasional smoker, and lived in a neighborhood that is 25-50% African American. (d) Percent change between model-based probability of having at least one poor health day in the past 30 days for the given discrimination domains compared to no discrimination. Table 5: Multilevel adjusted Models Predicting Self-assessed Poor Mental and Physical Health, by Race (a) Poor Mental Health (b) Black Latino (N = 461) (N = 382) E SE E SE Demographics Gender Female 0.20 0.25 0.49 0.26 Male (ref) -- -- -- -- Family income, $ <20,000 -0.07 0.40 -0.32 0.60 20,000-50,000 -0.11 0.39 -0.66 0.58 >50,000 (ref) -- -- -- -- Missing income -0.28 0.39 -0.36 0.60 Age 18-34 (ref) -- -- -- -- 35-54 0.16 0.27 -0.57 0.30 55+ -0.94 * 0.39 -0.84 0.47 65+ -0.78 0.43 -1.48 * 0.50 Education Less than high school 1.39 * 0.39 0.19 0.34 High school graduate 0.88 * 0.30 0.08 0.31 Any college (ref) -- -- -- -- Country of origin Puerto Rico (ref) -- -- Dominican Republic -0.45 0.37 Mexico -0.16 0.53 Other 0.09 0.32 Discrimination Types of discrimination No discrimination (ref) -- -- -- -- Racial only 0.65 * 0.34 0.59 0.45 Nonracial only 1.64 ** 0.32 1.25 ** 0.38 Racial and nonracial 0.88 * 0.39 0.42 0.38 Other Relevant Covariates Insurance No -0.14 0.45 -0.014 0.37 Yes (ref) -- -- -- -- Regular source of care No 0.60 0.36 -0.16 0.33 Yes (ref) -- -- -- -- Social support Low 0.36 0.35 -0.08 0.36 Medium 0.56 * 0.28 0.17 0.33 High (ref) -- -- -- -- Smoke Every day Some days Not at all (ref) Racial and ethnic composition Low (ref) -- -- -- -- Medium -0.35 0.40 1.02 * 0.45 High -0.70 * 0.36 0.90 * 0.38 Poor Physical Health (c) Black Latino (N = 461) (N = 382) E SE E SE Demographics Gender Female 0.05 0.24 -0.37 0.25 Male (ref) -- -- -- -- Family income, $ <20,000 0.96 * 0.41 -0.03 0.49 20,000-50,000 0.76 * 0.38 -1.34 * 0.51 >50,000 (ref) -- -- -- -- Missing income 0.28 0.41 -0.49 0.29 Age 18-34 (ref) -- -- -- -- 35-54 0.16 0.26 -0.21 0.29 55+ -0.67 0.46 0.09 0.45 65+ 0.39 0.38 -0.37 0.50 Education Less than high school 1.47 ** 0.37 0.04 0.32 High school graduate 0.45 0.29 -0.30 0.31 Any college (ref) -- -- -- -- Country of origin Puerto Rico (ref) -- -- Dominican Republic -0.53 0.36 Mexico -2.20 * 0.74 Other -0.23 0.33 Discrimination Types of discrimination No discrimination (ref) -- -- -- -- Racial only 0.04 0.33 0.00 0.47 Nonracial only 0.34 0.31 0.11 0.36 Racial and nonracial -0.39 0.36 0.13 0.35 Other Relevant Covariates Insurance No 0.30 0.45 0.94 * 0.42 Yes (ref) -- -- -- -- Regular source of care No 0.21 0.35 0.08 0.36 Yes (ref) -- -- -- -- Social support Low 0.08 0.33 -0.33 0.36 Medium 0.12 0.27 0.55 0.32 High (ref) -- -- -- -- Smoke Every day -0.41 0.30 0.22 0.35 Some days -0.07 0.38 0.20 0.39 Not at all (ref) -- -- -- -- Racial and ethnic composition Low (ref) -- -- -- -- Medium 0.09 0.40 0.02 0.39 High 0.32 0.37 -0.56 * 0.24 (a) Sample size is reduced by 30 respondents due to missing values. (b) poor mental health was defined as one or more poor mental health day in the past 30 days. (c) Poor physical health was defined as one or more poor physical health day in the past 30 days. ** p<.001, * p<.05.
We would like to thank two anonymous reviewers for their extremely thorough and helpful comments on an earlier version of this manuscript.
Amaro, H., N. F. Russo, and J. Johnson. 1987. "Family and Work Predictors of Psychological Well-being among Hispanic Women Professionals." Psychology of Women Quarterly 11 (4): 505-21.
Amaro, H., and A. de la Torre. 2002. "Public Health Needs and Scientific Opportunities in Research on Latinas." American Journal of-Public Health 92 (4): 525-29.
American Association for Public Opinion Research. 2000. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. Ann Arbor, MI: American Association for Public Opinion Research. Available at http://www.aapor.org/ default.asp?page=survey_methods/standards_and_best-practices /standard-definitions.
Anderson, R. T., P. Sorlie, E. Backlund, N. Johnson, and G. A. Kaplan. 1997. "Mortality Effects of Community Socioeconomic Status." Epidemology 8 (1): 42-7.
Andresen, E. M., T. Catlin, K. Wyrwich, and J. Jackson-Thompson. 2001. "Retest Reliability and Validity of a Surveillance Measure of Health-Related Quality of Life." Quality of Life Research 10 (3): 199.
Andresen, E. M., C. A. Fitch, P. M. McLendon, and A. R. Meyers. 2000. "Relability and Validity of Disability Questions for U.S. Census 2000." American Journal of Public Health 90 (8): 1297-9.
Benschop, R. J., and M. Schedlowski. 1999. "Acute Psychological Stress." In Psychoncuroimmunology, edited by M. Schedlowski, and U. Tewes pp. 293-306. New York: Kluwer Academic/Plenum.
Berkman, L. 1995. "The Role of Social Relations in Health Promotion. Psychosomatic Medicine 57 (3): 245-54.
Berkman, L., and I. Kawachi. 2000. Social Epidemiology. New York: Oxford University Press.
Bobo, L., and F. D. Gilliam. 1990. "Race, Sociopolitical Participation, and Blacks Empowerment." American Political Science Review 84 (2): 377-93.
Broman, C. L. 1996. "The Health Consequences of Discrimination: A Study of Blacks." Ethnicity and Disease 6 (1-2): 148-53.
Centers for Disease Control and Prevention. 1998. Sexually Transmitted Disease Surveillance 1997. Atlanta: Centers for Disease Control and Prevention.
--. 1999. HIV/AIDS Surveillance Report: U.S. HIV and AIDS Cases Reported through June 1999. Atlanta: Centers for Disease Control and Prevention.
Cochran, S. D., and V. M. Mays. 1994. "Depressive Distress among Homosexually Active African American Men and Women." American Journal of Psychiatry 151 (4): 524-9.
Collins, J. W., R.J. David, R. Symons, A. Handier, S. N. Wall, and L. Dwyer. 2000. "Low-Income African-Americans Mothers' Perception of Exposure to Racial Discrimination and Infant Birth Weight." Epidemiology 11 (3): 337-9.
Collins, K. S., A. Hall, and C. Neuhaus. 1999. U.S. Minority Health: A Chartbook. New York: Commonwealth Fund.
Diaz, R. M., G. Ayala, E. Bein, J. Henne, and B. V. Marin. 2001. "The Impact of Homophobia, Poverty, and Racism on the Mental Health of Gay and Bisexual Men: Findings from Three U.S. Cities." American Journal of Public Health 91 (6): 927-31.
Essed, P. 1992. Understanding Everyday Racism: An Interdisciplinary Theory. London: Sage.
Ferraro, K., and Y. P. Su. 2000. "Physician-Evaluated and Self-reported Morbidity for Predicting Disability." American Journal of Public Health 90 (1): 103-8.
Flegal, K. M., T. M. Ezzati, M. I. Harris, S. G. Haynes, R. Z. Juarez, W. C. Knowler, E. J. Perez-Stable, and M. P. Stem. 1991. "Prevalence of Diabetes in Mexican Americans, Cubans, and Puerto Ricans from the Hispanic Health and Nutrition Examination Survey, 1982-1984." Diabetes Care 14 (7): 628-38.
Flint, A. J., and T. E. Novotny. 1997. "Poverty Status and Cigarette Smoking Prevalence and Cessation in the United States, 1983-1993: The Independent Risk of Being Pour." Tobacco Control 6 (1): 14-8.
Galea, S., J. Hem, H. Resnick, D. Kilpatrick, M. Bucuvalas, J. Gold, and D. Vlahov. 2002. "Psychological Sequelae of the September 11 Terrorist Attacks in New York City." New England Journal of Medicine 346 (13): 982-7.
Gee, G. 2002. "A Multilevel Analysis of the Relationship between Institutional and Individual Racial Discrimination and Health Status." American Journal of Public Health 92 (4): 615-23.
Geronimus, A. T., J. Bound, T. A. Waidmann, M. M. Hillemeier, and P. B. Burns. 1996. "Excess Mortality among Blacks and Whites in the United States." New England Journal of Medicine 335 (21): 1552-8.
Gill, C. J. 1996. "Cultivating Common Ground: Women with Disabilities." In Manmade Medicine: Women's Health, Public Policy, and Reform, edited by K. L. Moss, pp. 183-93. Durham, NC: Duke University Press.
Halpern, D. 1993. "Minorities and Mental Health." Social Science and Medicine 36 (5): 597-607.
Halpern, D., and J. Nazroo. 2000. "The Ethnic Density Effect: Results from a National Community Survey of England and Wales." International Journal of Sociological Psychiatry 46 (1): 34-46.
Hirsch, B. J., and D. I. DuBois. 1992. "The Relation of Peers and Social Support and Psychological Symptomatology during the Transition to Junior High School. A Two-Year Longitudinal Analysis." American Journal of Community Psychology 20 (3): 333-47.
Idler, E., and IL Angel. 1990. "Self-rated Health and Mortality in the NHANES-I Epidemiologic Follow-up Study." 80 (4): 446-52.
Jackson, J. S., T. N. Brown, D. R. Williams, M. Torres, S. L. Sellers, and K. Brown. 1996. "Racism and the Physical and Mental Health Status of Blacks: A Thirteen-Year National Panel Study." Ethnicity and Disease 6 (1-2): 132-47.
James, S. A. 1994. "John Henryism and the Health of Blacks." Culture and Medicine in Psychiatry 18 (2): 163-82.
James, S. A., A. Z. LaCroix, D. G. Kleinbaum, and D. S. Strogatz. 1984. "John Henryism and Blood Pressure Differences among Blacks Men: The Role of Occupational Stressors." Journal of Behavioral Medicine 7 (3): 259-75.
James, S. A., D. S. Strogatz, S. B. Wing, and D. L. Ramsey. 1987. "Socioeconomic Status, John Henryism, and Hypertension in Blacks and Whites." American Journal of Epidemiology 126 (4): 664-73.
Jary, D., and J. Jary. 1995. Collins Dictionary of Sociology, 2d ed. Glasgow, UK: HarperCollins.
Kaplan, C. P., and T. L. Van Valey. 1980. Census "80: Continuing the Fact Finding Tradition. Washington, DC: U.S. Government Printing Office.
Kessler, R. C., K. D. Mickelson, and D. R. Williams. 1999. "The Prevalence, Distribution, and Mental Health Correlates of Perceived Discrimination in the United States." Journal of Health and Social Behavior 40 (3): 208-30.
Kish, L. A. 1949. "Procedure for Objective Respondent Selection within the Household." Journal of the American Statistical Association 44: 380-7.
Krieger, N. 1990. "Racial and Gender Discrimination: Risk Factors for High Blood Pressure?" Social Science and Medicine 30 (12): 1273-81.
--. 2000. "Discrimination and Health." In Social Epidemiology, edited by L. Berkman and I. Kawachi, pp. 36-75. Oxford: Oxford University Press.
Krieger, N., D. Rowley, A. Hermann, B. Avery, and M. T. Phillips. 1993. "Racism, Sexism, and Social Class: Implications for Studies of Health, Disease, and Well-being." American Journal of Preventive Medicine (9, supplement 6): 82-122.
Kreiger, N., and S. Sidney. 1996. "Racial Discrimination and Blood Pressure: The CARDIA Study of Young Blacks and White Adults." American Journal of Public Health 86 (10): 1370-8.
--. 1997. "Prevalence of Health Implications of Anti-Gay Discrimination: A Study of Blacks and White Women and Men in the Cardia Cohort. International Journal of Health Services 27 (1): 157-76.
Krieger, N., D. R. Williams, and N. E. Moss. 1997. "Measuring Social Class in the U.S. Public Health Research: Concepts, Methodologies, and Guidelines." Annual Review of Public Health 18: 341-78.
Ladrine, H., and E. A. Klonoff. 1996. "The Schedule of Racist Events: A Measure of Racial Discrimination and Study of Its Negative Physical and Mental Health Consequences." Journal of Black Psychology 22 (2): 144-68.
Ladrine, H., E. A. Klonoff, J. Gibbs, V. Manning, and M. Lund. 1995. "Physical and Psychiatric Correlates of Gender Discrimination: An Application of the Schedule of Sexist Events." Psychology of Women Quarterly 19 (4): 473-92.
Lille-Blanton, M., and T. LaVeist. 1996. "Race/Ethnicity, the Social Environment, and Health." Social Science and Medicine 43 (12): 83-92.
Mayberry, R. M., F. Mill, and E. Ofili. 2000. "Racial and Ethnic Differences in Access to Medical Care." Medical Care Research and Review 57 (1): 108-45.
Mays, V. M., and S. D. Cochran. 2001. "Mental Health Correlates of Perceived Discrimination among Lesbian, Gay, and Bisexual Adults in the United States." American Journal of Public Health 91 (11): 1869-76.
Meyer, I. H. 1995. "Minority Stress and Mental Health in Gay Men." Journal of Health and Social Behavior 36 (1): 38-56.
National Center for Health Statisitics. 1997. Health, United States, 1996-1997, and Injury Chartbook Hyattsville, MD: National Center for Health Statistics.
Navarro, V. 1990. "Race or Class versus Race and Class: Mortality Differentials in the United States." Lancet 336 (8725): 1238-40.
Northridge, M. E., A. Morabia, M. L. Ganz, M. T. Bassett, D. Gemson, H. Andrews, and C. McCord. 1998. "Contribution of Smoking to Excess Mortality in Harlem." American Journal of Epidemiology 147 (3): 250-8.
Public Health Special. 2001. Racial and Ethnic Discrimination in Health Care Settings.
Ren, X. S., and B. C. Amick. 1996. "Racial and Ethnic Disparities in Self-assessed Health Status: Evidence from the National Survey of Families and Households." Ethnic Health 1 (3): 293-303.
Rhodes, J. E., J. M. Contreras, and S. C. Mangelsdorf. 1994. "Natural Mentor Relationships among Latina Adolescent Mothers: Psychological Adjustment, Moderating Processes, and the Role of Early Parental Acceptance." American Journal of Community Psychology 22 (2): 211-27.
Rowley, D. L. 1994. "Research Issues in the Study of Very Low Birthweight and Preterm Delivery among African-American Women." Journal of the National Medical Association 86 (10): 761-4.
Salgado de Snyder, V. N. 1987. "Factors Associated with Acculturative Stress and Depressive Symptomatology among Married Mexican Immigrant Women." Psychology of Women Quarterly 11 (4): 475-88.
Sherbourne, C. D., and A. L. Stewart. 1991. "The MOS Social Support Survey." Social Science and Medicine 32 (6): 705-14.
Vaid, U. 1995. Virtual Equality: The Mainstreaming of Gay and Lesbian Liberation. New York: Anchor Books.
Williams, D. R. 1999. "Race, Socioeconomic Status, and Health: The Added Effects of Racism and Discrimination." In Annals of the New York Academy of Sciences, edited by N. E. Adler, M. Marmot, B. S. McEwen, and J. Stewart, pp. 173-88. New York: New York Academy of Sciences.
Williams, D. R., and C. Collins. 1995. "U.S. Socioeconomic and Racial Differences in Health: Patterns and Explanations." Annual Review of Sociology 21: 349-86.
--. 2001. "Racial Residential Segregation: A Fundamental Cause of Racial Disparities in Health." Public Health Reports 116 (5): 404-16.
Williams, D. R., M. C. Spencer, and J. S. Jackson. 1999. "Races, Stress, and Physical Health: The Role of Group Identity." In Self, Social Identity, and Physical Health, edited by R.J. Contrada and R. D. Ashmore, pp. 71-100. New York: Oxford University Press.
Williams, D. R., Y. Yan, J. S. Jackson, and N. Anderson. 1997. "Racial Differences in Physical and Mental Health: Socio-economic Status, Stress, and Discrimination." Journal of Health Psychology 2 (3): 335-51.
Wingo, P. A., L. A. G. Ries, H. M. Rosenberg, D. S. Miller, and B. K. Edwards. 1998. "Cancer Incidence and Mortality, 1973-1996." Cancer 82 (6): 1197-207.
Zimmer, Z., J. Natividad, H. S. Lin, and N. Chayovan. 2000. "A Cross-national Examination of the Determinants of Self-assessed Health." Journal of Health and Social Behavior 41 (4): 465-81.
This work was funded by grant no. DA14219-02S1 from the National Institute on Drug Abuse, Cooperative Agreement R18-CCR22983-01 from the Centers for Disease Control and Prevention, and by a grant from The Robert Wood Johnson Foundation.
Address correspondence to Jennifer Stuber, Ph.D., The Division of Health and Science Policy, The New York Academy of Medicine, 1216 Fifth Ave., New York, NY 10029-5283. Sandro Galea, M.D., M.P.H, Jennifer Ahern, M.P.H., Shannon Blaney, M.P.H., and Crystal Fuller, Ph.D., are with the Center for Urban Epidemiologic Studies, The New York Academy of Medicine, New York, NY. Additionally, Dr. Fuller is with the Mailman School of Public Health, Columbia University, New York, NY.
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Planning Tips
Admissions Tip: Considering the Campus
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CMU / Tepper, 11:59pm EST
Monday, January 5th:
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UPenn / Wharton, 5:00pm EST Continue reading…
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Admissions Tip: Plan of Attack
With November underway, Round Two deadlines for a number of programs are just around the corner. As most applicants are targeting multiple schools and still working to narrow down their school selection, we wanted to take some time today to stress the importance of taking a deep breath and a step back and formulating a timeline for the coming weeks. Establishing a set of incremental goals with regard to essay composition and recommender management at this point in the season will help you to avoid feeling overwhelmed and ensure that your aims are realistic.
One of our most important pointers pertains to the process of writing essays. The urge to make progress on multiple fronts leads many applicants to work on essays for several schools in parallel, an approach that can be problematic. One reason for this is that when one spends time immersed in three sets of essays at once, it's easy to lose sight of the full picture he or she is presenting to any one school. While it's important to be oneself in the application process, it's also crucial that an applicant tailor his or her materials to each school, a process that is made harder when constantly going back and forth among responses for various programs. Another issue is that it's easy to waste time implementing the same edits across documents for multiple schools, or to lose track of what one has changed in which essay. For these reasons, we generally recommend focusing one's full essay-writing attention on one program at a time. Continue reading…
Admissions Tip: Interviewing the Interviewer
We've been offering a good deal of advice lately on how to conduct oneself and prepare responses to MBA interview questions. Today we'd like to highlight the importance of thinking about what you might ask. Virtually all business school interviewers conclude their discussion by offering the applicant a chance to ask some questions about the program. While it might be tempting to claim that you've already learned all you need to know about the school, this is actually a great opportunity to gain additional insight, show your enthusiasm about a specific element of the curriculum or community, and demonstrate that you appreciate the opportunity to learn from your interviewer's experiences.
Here are a few simple guidelines to keep in mind while thinking about what you might ask:
1. Focus on the positive. Now is not the time to conduct due diligence or express skepticism about a school's academic program or career resources. You're still marketing yourself to the adcom at this stage of the process, so you'll want to project enthusiasm and demonstrate a desire to become more familiar with a program's merits and your potential fit. Continue reading…
Admissions Tip: MBA Interview Prep
With interview invitations from a number of programs already on their way out to Round One applicants, we wanted to offer some more advice on this element of the admissions process. Last week we posted some very basic etiquette information that will help candidates ensure that everything is in order on the big day. Today, we turn our attention to some steps one can take to prepare for the interview itself.
1) Know what to expect. This might go without saying, but interview types and duration vary across programs. For instance, nearly all invited Stanford applicants interview with alumni, while on-campus Wharton interviews are conducted by second-year students and admissions staff. Candidates for Columbia admission participate in an informative resume-based chat, while HBS and MIT interviewers have in-depth knowledge of the applicant's entire file. Thinking carefully about the format of the interview and the person conducting it will influence the sort of questions you might come prepared to ask and help you arrive at a mindset conducive to success. Continue reading…
Admissions Tip: MBA Interview Etiquette
With interviews imminent for Round One applicants, we wanted to turn our attention to this important step in the admissions process and share a few very basic pointers on interview etiquette. Though the content of your application materials and comments during the interview are of paramount importance, it's also crucial to put one's best foot forward and make a positive initial impression. Here are a few guidelines for interviewing applicants to keep in mind:
1) Dress the part. Unless meeting with an alum who explicitly specifies a more casual dress code, assume that business attire is appropriate. We recommend that applicants dress conservatively, opting for a dark suit (pants or skirts are both fine for women) and a blue or white shirt. Steer clear of flashy brand gear and loud ties, and go easy on makeup and fragrances; you want to be remembered for what you say and who you are, not what you wore. Continue reading…
Admissions Tips: Common Recommendation Dilemmas
As many of our readers are aware, letters of recommendation are a central part of the application process. We would like to take a look at how to handle the snags that often arise for applicants in unique employment situations.
The applicant who is most likely to have trouble finding a suitable recommender is either self-employed or works in his or her family's business. First, self-employed entrepreneurs by their very nature do not have a direct supervisor. Similarly, an applicant who works for the family business may have trouble finding a non-related supervisor, or someone who can offer a truly objective opinion. Continue reading…
Admissions Tip: Applying to Business School as a Younger Applicant
As many of our readers know, it has become increasingly common for younger individuals to apply to MBA programs. Whereas the average age and years of work experience at the leading business schools has traditionally hovered at around 28 and five respectively, many programs are now carefully considering the more youthful end of the applicant pool. Of course, the fact that admissions officers are taking a closer look at younger applicants does not mean that getting accepted to a top program is easy for this group. In fact, it may be difficult for younger applicants to present themselves as fully prepared to contribute to an MBA program because they often lack leadership experience and extended business exposure. This is especially true as they will be compared to their fellow applicants who have more years in the working world (often translating to more leadership experience and professional accomplishments). With this in mind, we'd like to offer a few tips that will help younger MBA candidates leverage the strengths of their candidacies and become increasingly competitive applicants at their choice schools. Continue reading…

subset: commoncrawl


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Within a week of surrender of her husband and top Maoist leader Ponnojula Parameswara Rao alias Nandu, a member of Galikonda area committee Seendri Chandri alias Swaroopa surrendered before Superintendent of Police Vineet Brij Lal here on Saturday.
Nandu had surrendered on March 28 owing to ill health: he is a severe diabetic, and Swaroopa is suffering from spondilits. "We have surrendered only due to ill health", Swaroopa, who carried a reward of Rs. 2 lakh on her head, told reporters. She was staying at Lenjuguda near Janbai in Orissa and had stayed there till coming to learn Nandu's surrender.
From 2005 Swaroopa had wanted to leave the party due to ill health but its east division secretary Ganesh had wanted her to stay back since her husband was active in the party.
Her husband and she would go to her in-laws' native village Hasanparthi in Warangal district and settle down in life with the help of reward on their heads (Nandu carried a reward of Rs. 5 lakh on his head) the Government had decided to give them, Swaroopa said. A rehabilitation package would also be given to the couple. Mr. Brij Lal said she would be provided medical treatment also. Nandu had been examined at a corporate hospital here after his surrender and was told to get admitted for treatment. But he had wanted to be nearer to his family and preferred treatment at Warangal or Hyderabad.
Mr. Brij Lal said a few more Maoists were likely to surrender shortly. Around five of them had quit the party and they were reportedly trying to contact the officials.
Daughter of an agriculture labourer of Kindangi (Boodaralla Panchayat) in Koyyuru mandal, Swaroopa decided to join naxalites in 1996 at the age of 15 when her parents wanted to perform her marriage with a cousin much older. She was also influenced by the speeches of a top Maoist leader Sattenna and Nandu. She was with the Tandava dalam and when she and Nandu wanted to get married the party performed the marriage in 1998 at Mikudupalli village. A few thousands of people had reportedly attended the marriage.
Swaroopa was promoted to area committee member and was sent to the Galikonda AC. She was involved in the incidents of blasting of Forest Office building at RV Nagaram in 1997 and the Puttakota exchange of fire.


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CES, this Casio TRYX imager sports two hinges for the sake of versatility: one for the frame that doubles as a handle or a stand, and one for swiveling the display -- presumably a touchscreen since there are just a couple of buttons on the body. Coincidentally, Google also led us to the TRYX's stash of press shots which have now been pulled, but we've got you guys a screenshot for the time being -- it's not like it'll be long before all is unveiled, anyway.
Gallery | 5 Photos
Casio shows off multi-hinged TRYX camera outside CES, prematurely outs press shots


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Rachel Hartigan SheaAny novel about a depressed person, even one set in an imagined world, can be tedious at times. Lessing's Ifrik -- with its bands of emaciated and glassy-eyed refugees, its communities willfully blind to the calamities of war and drought that stalk them, its dearth of gentleness -- is the more compelling character here, one worth meeting as we ponder what our own climate change has in store for us.
— The Washington Post


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Red Feather Lakes
Forest Service Sets Christmas Tree Cutting Dates In Colorado
The U.S. Forest Service is planning to open five Christmas tree cutting areas along the Front Range next month.
Rainbow Family Stabbing Raises Concern Among First Responders
They preach peace and non-violence but a stabbing at the Rainbow Family gathering has first responders changing their policy in regards to that camp.
Arrest Made In Red Feather Lakes Stabbings
Larimer County authorities have arrested a 37-year-old man in connection with three stabbings at Red Feather Lakes.
Campground At Red Feather Lakes Reopens After Last Month's Storms
A section of Red Feather Lakes northwest of Fort Collins is expected to reopen to campers.
Campground At Red Feather Lakes Closed After 200 Trees Downed By Storm
There were evacuations due to weather near Red Feather Lakes northwest of Fort Collins on Thursday.
T-Storms, But No Twisters Forecast For Colorado
Spring thunderstorms could bring some heavy rain to parts of Colorado Friday, but thankfully there are no more tornadoes or golf ball-sized hail in the forecast.
Best Yoga Retreats In The U.S.
While living in today's world can often be stressful, if not overwhelming, a visit to a yoga retreat can sharply improve one's overall fitness and revitalize the body and mind.
New Forests, New Fire Hazards After Beetle Kill
In the Roosevelt National Forest northwest of Fort Collins, subalpine fir trees and aspens have started to grow in the shadows of dead lodgepole pine trees. It is becoming a new forest, with new hazards.
1 Dead After Shooting Involving Larimer County Sheriff's Deputies
Larimer County sheriff's officials say a man is dead after a shooting involving a sheriff's deputy, who wasn't injured.
Fire Near Red Feather Lakes Destroys Cabin
Evacuation notices have been lifted for residents in northern Colorado following a wildfire that destroyed a summer cabin overnight.


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Intern Black Book is a step-by-step program showing entrepreneurs how to harness the unlimited potential of unpaid interns. Following this program will not only lighten your workload but it will provide you with the extra time needed to develop and improve upon your own business.
Intern Black Book is perfect for the one-or-two-person business. After all, the one-man entrepreneur is the one who needs the most help taking care of basic tasks. You don't need an office and you defiantly don't need a Human Resources department. All you need is a Business or even just an idea for one, and Intern Black Book will do the rest.
The Intern Black Book is all-inclusive. It provides proven methods, tips and solutions for every step in the hiring process as well as what to do after you've hired your interns.


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(Science: immunology) fragment of immunoglobulin prepared by papain treatment. Fab fragments (45 kD) consist of one light chain linked through a disulphide bond to a portion of the heavy chain and contain one antigen binding Site. They can be considered as univalent antibodies.
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With her phenomenal Mage Winds trilogy, bestselling author Mercedes Lackey captivated fans across the country. Now in the first volume of the series sequel, she continues the same storyline, returning readers to a war-torn Valdemar in preparation to confront an ancient Eastern Empire - ruled by a monarch whose magical tactics by be beyond any sorcery known to the western kingdoms.
©1994 Mercedes R. Lackey (P)2012 Audible, Inc.
"Great story! Slow narration."
This is one of my favorite books, you have no idea how happy I am to have it in audiobook! Karral's journey from horse boy to secretary to well where he ends up is a journey I love to take over and over.
I was slightly disappointed in the performance however. The narration is slow. Just enough to frustrate me. Thankfully I listen on audible's app and I was able to increase the speed to 1 and 1/2 speed. This makes all but a few sentences sound normal paced. Those sentences usually being an excited quote. Anyway thanks to this feature I was able to listen and love one of my favorite books.
"disappointing narration"
It was hard to listen to the narrator, who had a cold. Also his character voices were lame, and he even mispronounced key terms in the story...Very disappointing.
"I dunno.."
I actually liked the narrator.
...what a silly question. This is book 1 of a trilogy that takes place directly after a trilogy that takes place a few years after a trilogy. There are endings?
I can do the dishes while reading
A lot of people complain about this narrator but he's better than the one they got for the last herald Mage trilogy who mispronounced simple English words like bow. I could do without the raspy voice for some characters but otherwise I found him enjoyable to listen to.
"Story good but.Speed up your listening speed"
Thought the story line started slow it made sense after you getting further in the plot.I increased the narrator speed and was more comfortable with listening. I will listen again, I also bought book 2 and ,. with the same narrator plan to speed up those also.
Slow Slow Slow
Once the narrator was speeded up.
"Poor reading"
I am just starting this book and am struggling with the reader. HORRIBLE!! I will stick it out since I love the series but will not purchase any more books by the reader. Poor inflection and emphasis on work words. Terrible.
"great fantasy"
Always enjoy stories about Valdemar. This one carries forward interesting characters into adventures in lands of new frontiers ,people, and places. Have ordered book two in the series.
"Poor Narrator! Great Story"
This was a book I read years ago when first published. I loved the story.
Karal. A young man poised for great things who has normal concerns.
He made an exciting book almost boring!
Get a different narrator!
"Wonderful story brought low by the narrator"
Yes, the narrator. He doesn't have a wide enough range for a book with a large cast.
The narrator is weird choice for this series. He have a lovely deep voice, but this a series with a very young man as the lead. But my biggest problem is he seemed to only have two ways of differentiating characters - either making his already deep voice even deeper and more booming or making his voice whispery sinister. I can get past any pronunciation issues, that sort of thing does not knock me out of the story but it took me well into the 2nd book of the series to get over the voices. A voice that is described in text as a young mans voice should not be preformed as wheezy asthmatic and elderly
"narrator needs to speed up"
I love this book and was happy to see it available in Audible but this narrator is horrible! He is so slow that I have had to speed up the narration to 1 1/2 X normal speed in order to listen to it.
"Love all her books"
Love this entire series, and Audible needs to get all her books. The only part I don't like is the voice used for the griffins, its just not right. But otherwise the performance is very good.
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Originally written on Fight Hub TV | Last updated 11/8/14
From the Mandalay Bay Events Center comes a solid card that could shape the future of our MMA programming for quite a while. Here are the weigh-n results, with only Maximo Blanco having trouble making weight. FX channel main card (9 p.m. ET): Bantamweight Urijah Faber (136) vs. Scott Jorgensen (135) Middleweight/TUF FinalUriah Hall (185) vs. Kelvin Gastelum (186) Heavyweight: Travis Browne (239) vs. Gabriel Gonzaga (261) Bantamweight: Miesha Tate (135) vs. Cat Zingano (136) Middleweight: Bubba McDaniel (184) vs. Gilbert Smith (185) FUEL TV Prelims (7 p.m. ET): Middleweight: Josh Samman (184) vs. Kevin Casey (185) Middleweight: Luke Barnatt (185) vs. Collin Hart (184) Middleweight: Dylan Andrews (186) vs. Jimmy Quinlan (185) Middleweight: Clint Hester (185) vs. Bristol Marunde (185) Facebook Prelims (5:30 p.m. ET): Featherweight: Bart Palaszewski (145) vs. Cole Miller (146) Featherweight: Daniel Pineda (145) vs. Justin Lawrence (145) Featherweight: Maximo Blanco (147) vs. Sam Sicilia...
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StartUpHire is the place to find DIRECTOR OF PRODUCT MANAGEMENT jobs. Review the list of DIRECTOR OF PRODUCT MANAGEMENT career opportunities to the left. These DIRECTOR OF PRODUCT MANAGEMENT jobs represent some of the most rewarding careers available.
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Don't see the employment opportunity you're looking for? Check back soon for more DIRECTOR OF PRODUCT MANAGEMENT jobs, or sign up for an email alert (link below) to receive periodic updates on new DIRECTOR OF PRODUCT MANAGEMENT jobs.
The role of the Director of Product Engineering is to lead and drive the product development of Aquion battery through module level products offerings, including battery monitoring systems.5/19/2015 Pittsburgh, PA Save This Job
Director of Product ManagementClick here to see all openings!Company Overview:Velocify is one of the fastest growing software-as-a-service companies in the country, providing the market-leading solution for intelligent sales automation and cloud-based telephony. Velocifyâs5/24/2015 El Segundo, CA Save This Job
You know LivingSocial as the marketplace to buy and share the best things to do in your city and beyond. And while we're bringing excitement and value to our merchants and consumers, we're also committed to creating a world-class environment for our employees. Our talented4/21/2015 Washington, DC Save This Job
Moki is looking for a Senior Director of Product Management with strong leadership and communication skills. The ideal candidate is detail-oriented and able to effectively manage tactical day-to-day priorities toward a strategic product vision. Moki is located in a prime business3/30/2015 Lehi, UT Save This Job
We are seeking a high caliber Senior Product Manager or Director of Product Management to join our growing team in Sunnyvale. This person will be tasked with the planning and execution throughout the product lifecycle, including: providing input into the product vision,4/28/2015 Sunnyvale, CA Save This Job
Job Number:001341 Job Title:Director of Product Management - Supply Planning Number of Openings:1 Job Type (Employment Type):Direct Hire Start Date:2015/02/11 Country:United States State/Province:Texas City:Austin Salary Interval:Per Year Job Schedule:Full Time Job2/28/2015 Austin, TX Save This Job
Company at a Glance OpenX is a global leader in digital advertising technology. OpenX?s mission is to maximize advertising revenue for digital media companies on any connected screen. The company offers a unified monetization platform that includes an ad server, a global4/29/2015 Los Angeles, CA Save This Job
San Francisco, CA Product Management Job Description At Technorati we are building a new advertising technology solution for publishers to accelerate their true revenue opportunity. If you?re a strategic product leader who wants to reshape the way publishers manage programmatic4/13/2015 San Francisco, CA Save This Job
Location:Birmingham, AL Job Code:811 # of openings:1 Description Recently ranked by Healthcare Informatics as one of the Top 100 Healthcare IT companies, SourceMedical provides outpatient information solutions and services for ambulatory surgery centers, surgical hospitals, and4/4/2015 Birmingham, AL Save This Job
ID 2015-1626 # of Openings 1 Job Locations US-PA-Exton Posted Date 4/21/2015 Category Information Technology More information about this job: Overview: iPipeline is a leading provider of cloud-based software solutions for the life insurance industry. Through our SaaS5/7/2015 Exton, PA Save This Job
At Pandora, we're a unique collection of engineers, musicians, designers, marketers, and world-class sellers with a common goal: to enrich lives by delivering effortless personalized music enjoyment and discovery. Peopleâthe listeners, the artists, and our employeesâare4/30/2015 Oakland, CA Save This Job
Represent the voice of the customer throughout all aspects of ShareThis? business Be an expert with respect to competitors and overall market landscape Work cross functionally to manage all phases of the product lifecycle Ensure product design, development, and testing efforts3/30/2015 Palo Alto, CA Save This Job
Computers/Software Palo Alto, CA, United States Apply Send Jobvite RESPONSIBILITIES Represent the voice of the customer throughout all aspects of ShareThis? business Be an expert with respect to competitors and overall market landscape Work cross functionally to manage all3/30/2015 Palo Alto, CA Save This Job
You have demonstrated an ability to ship and grow exceptional consumer products. You're looking for a role where you can define the product direction of a company and shape the growth of the product management team.4/28/2015 San Francisco, CA Save This Job
Tracking Code 308-621 Job Description GetWellNetwork, Inc. provides patient engagement solutions that help health care providers engage, educate and empower patients along the care continuum. Our patient-centered platform, delivered across multiple technology platforms including4/13/2015 Bethesda, MD Save This Job
New York A Little About Crowdtap: Crowdtap is a fast-growing, profitable, VC-backed startup that has been named one of America's 100 Most Promising Companies in Forbes in 2013 & 2014. Our platform makes it easy for Fortune 500 brands to inspire consumers to create authentic3/31/2015 New York, NY Save This Job
The Company: TimeTrade is a leading venture-backed SaaS company that is undergoing rapid growth and adoption of it?s industry leading PurchasePath platform. Sprint, Best Buy, Sears, Lowes, J. Crew, David?s Bridal, JC Penny, LegalZoom and over 240 other companies use TimeTrade3/29/2015 Tewksbury, MA Save This Job
The MuleSoft Products team seeks a strategic product leader that can drive growth through API-led connectivity across the enterprise application and IT infrastructure space. Doing so requires innovation around how connectivity is delivered. In todayâs API-first economy, a5/13/2015 San Francisco, CA Save This Job
The Director of Product Marketing will collaborate with Product Management, Sales, Marketing, and Account Management to successfully launch and maintain products using meaningful market insight to shape our positioning and feature development.5/1/2015 Mountain View, CA Save This Job
The Director of Product Marketing will collaborate with Product Management, Sales, Marketing, and Account Management to successfully launch and maintain products using meaningful market insight to shape our positioning and feature development.5/1/2015 Mountain View, CA Save This Job


text: 000 FXXX06 KWNP 180425 WEKHIL :Product: 7-day Space Weather Highlights :Issued: 2015 May 18 0402 UTC # Prepared by the US Dept. of Commerce, NOAA, Space Weather Prediction Center # Product description and SWPC contact on the Web # http://www.swpc.noaa.gov/wwire.html # # 7-day Space Weather Highlights # Highlights of Solar and Geomagnetic Activity 11 - 17 May 2015 Solar activity was low from 11-15 May. The largest event of the period was a C9/1n at 13/1838 UTC associated with a filament eruption near Region 2345 (N16, L=113, class/area Axx/020 on 13 May). Region 2339 (N13, L=129, class/area Fkc/900 on 08 May) was the most active region on the disk and produced 19 C-class events during the period. There were several filament eruptions observed during the period however, none of them turned out to be geoeffective. Solar activity decreased to very low levels for 16-17 May. The greater than 10 MeV proton flux at geosynchronous orbit was enhanced on 12 May reaching a peak flux of 6 pfu at 12/0720 UTC but never crossed alert threshold. The greater than 2 MeV electron flux at geosynchronous orbit was at normal to moderate levels on 11-13 May before reaching high levels daily for the remainder of the period in response to effects from a negative polarity coronal hole high speed stream (CH HSS). Geomagnetic field activity was at unsettled to active levels on 11 May and mostly unsettled levels on 12 May due to extended periods of negative Bz. On 13 May, geomagnetic field conditions increased to active to major storm levels due to the onset of a recurrent negative polarity CH HSS. Quiet to unsettled levels persisted on 14-15 May as effects from the HSS subsided. Quiet conditions prevailed for the remainder of the period.


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I do stuff on UT2004. Most are weapons or vehicles. Also did some characters. Never thought about publishing it as it depended on lots of custom content. I am currently waiting for feedback to make standalone versions and release them here.
Almost everyone heard of Asterix & Obelix so this group is for their fans!!
If you love Call of Duty games this is the place for you.
This is a group for MW3 fans We will be posting all the news about the game here
Call of Duty Fans and Players is an open group for all Call of Duty players and fans alike from all over the world.


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Morganfield, Kentucky
|Morganfield, Kentucky
Main Street downtown
Location of Morganfield within Kentucky.
|Coordinates: 37°41′4″N 87°54′43″W / 37.68444°N 87.91194°WCoordinates: 37°41′4″N 87°54′43″W / 37.68444°N 87.91194°W
|United States
|January 6, 1812[1]
|March 16, 1870[1]
|Named for
|Daniel Morgan[2]
|• Mayor
|Jerry R. Freer
|• Total
|2.1 sq mi (5.5 km2)
|• Land
|2.1 sq mi (5.4 km2)
|• Water
|0.0 sq mi (0.1 km2)
|437 ft (134 m)
|Population (2010)
|• Total
|Time zone
|• Summer (DST)
|ZIP Code
|Area code(s)
|270 & 364
|FIPS code
|GNIS feature ID
Morganfield is a home rule-class city[3] in Union County, Kentucky, in the United States. It is the seat of its county.[4] The population was 3,285 as of the year 2010 U.S. census.
The city was named for Revolutionary War General Daniel Morgan, who received the land the city now sits on in return for his military service.[2]
Morganfield is located at 37°41′04″N 87°54′43″W / 37.684502°N 87.911893°W.[5] According to the United States Census Bureau, the city has a total area of 2.1 square miles (5.5 km²), of which 2.1 square miles (5.4 km²) is land and 0.04 square mile (0.1 km²) (1.42%) is water.
As of the census[6] of 2000, there were 3,494 people, 1,434 households, and 926 families residing in the city. The population density was 1,671.5 people per square mile (645.5/km²). There were 1,581 housing units at an average density of 756.3 per square mile (292.1/km²). The racial makeup of the city was 82.34% White, 16.23% African American, 0.03% Native American, 0.17% Asian, 0.17% from other races, and 1.06% from two or more races. Hispanic or Latino of any race were 0.86% of the population.
There were 1,434 households out of which 28.0% had children under the age of 18 living with them, 47.6% were married couples living together, 14.1% had a female householder with no husband present, and 35.4% were non-families. 32.4% of all households were made up of individuals and 15.1% had someone living alone who was 65 years of age or older. The average household size was 2.37 and the average family size was 3.01.
In the city the population was spread out with 24.5% under the age of 18, 9.3% from 18 to 24, 24.8% from 25 to 44, 24.7% from 45 to 64, and 16.7% who were 65 years of age or older. The median age was 39 years. For every 100 females there were 84.9 males. For every 100 females age 18 and over, there were 81.7 males.
The median income for a household in the city was $38,676, and the median income for a family was $52,864. Males had a median income of $32,831 versus $22,736 for females. The per capita income for the city was $19,251. About 9.9% of families and 12.3% of the population were below the poverty line, including 19.2% of those under age 18 and 15.1% of those age 65 or over.
Notable people[edit]
- Dwane Casey – Currently the Head Coach for the NBA's Toronto Raptors. Former College Basketball player and assistant coach for the Kentucky Wildcats, and former Minnesota Timberwolves head coach
- Earle C. Clements – Former U.S. Senator, Former Governor of Kentucky and Former U.S. Representative
- Kassie DePaiva – Soap opera actress known for her roles on Guiding Light as Chelsea Reardon and best known for One Life to Live and General Hospital as Blair Cramer Manning
- Mallory Ervin – Miss Kentucky 2009 and 4th Runner-up at Miss America 2010
- Ed Johnson – Former Baseball Player
- Larry Johnson – Former Basketball Player
- Jerry Wayne Parrish – Former United States Army Corporal; died in Pyongyang, North Korea
- Dottie Rambo – Gospel singer/song writer
- John Selby Townsend – Iowa jurist and legislator
Camp Breckinridge[edit]
Morganfield is located near Camp Breckinridge, a World War II prisoner of war camp. During the Korean War, Camp Breckinridge was the headquarters of the 506th Regiment of the 101st Airborne Division and was used for the conduct of basic training of newly inducted U.S. Army recruits. The Associated Press reported on May 22, 2007, a continuing dispute regarding the families of displaced residents of the area that became Camp Breckenridge and the U.S. government. Former U.S. Supreme Court Justice Sandra Day O'Connor was named as a mediator in the dispute in February 2007.
About 40,000 soldiers preparing for the war stayed at the camp. The camp also held about 3,000 German prisoners of war before being deactivated in 1949.
It was re-opened in 1965 as the "Breckinridge Job Corps Center", and the name was later changed to the "Earle C. Clements Job Corps Center" in 1980 to honor Earle C. Clements, a former Kentucky governor and U.S. senator.
Morganfield is served by the Union County Public Schools, Kentucky. Elementary students attend Morganfield Elementary. Middle and High School students attend Union County Middle School & Union County High School located outside Morganfield. A parochial school, St Ann is located in Morganfield. The Earle C. Clements Job Corps facility is also located in Morganfield.
The climate in this area is characterized by hot, humid summers and generally mild to cool winters. According to the Köppen Climate Classification system, Morganfield has a humid subtropical climate, abbreviated "Cfa" on climate maps.[7]
- ^ a b Commonwealth of Kentucky. Office of the Secretary of State. Land Office. "Morganfield, Kentucky". Accessed 26 Aug 2013.
- ^ a b "Dictionary of Places: Morganfield". Encyclopedia of Kentucky. New York, New York: Somerset Publishers. 1987. ISBN 0-403-09981-1.
- ^ "Summary and Reference Guide to House Bill 331 City Classification Reform" (PDF). Kentucky League of Cities. Retrieved December 30, 2014.
- ^ "Find a County". National Association of Counties. Retrieved 2011-06-07.
- ^ "US Gazetteer files: 2010, 2000, and 1990". United States Census Bureau. 2011-02-12. Retrieved 2011-04-23.
- ^ "American FactFinder". United States Census Bureau. Retrieved 2011-05-14.
- ^ Climate Summary for Morganfield, Kentucky
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- Cities in Kentucky
- Cities in Union County, Kentucky
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- County seats in Kentucky
- Military facilities in Kentucky
- Populated places established in 1812
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