Socioeconomic Status Distribution Research Articles

Neighborhood-Level Socioeconomic Predictors of Nonadherence to Mammography Screening Guidelines

As neighborhood context is increasingly recognized as an important predictor of health outcomes and health behaviors, this analysis sought to determine the relationship between neighborhood-level socioeconomic status (SES) and regular mammography screening behavior. One thousand four hundred fifty-one women ages 40 to 79 years who obtained an "index" screening mammogram at one of five urban hospitals in Connecticut between October 1996 and January 1998 were enrolled in this prospective study. The logistic regression analysis includes the 1,229 women [484 African-American (39%) and 745 White (61%)] who completed telephone interviews at baseline and follow-up (average 29.4 months later) and for whom the study outcome, nonadherence to age-specific mammography screening guidelines, was ascertained. Neighborhood-level SES was determined using 1990 census tract information. Neighborhood-level SES variables (quartiles) were associated with nonadherence for African-American women [neighborhood-level education and composite socioeconomic position index (SEP Index)] and White women (neighborhood-level crowding and neighborhood-level assets). Using race-specific categorizations reflective of individual-level SES distributions, the SEP Index and neighborhood-level education were associated with nonadherence to mammography screening guidelines for African-American women (marginally significant for White women), independent of individual-level SES and other known predictors of mammography screening use [African-American women: SEP Index odds ratio (OR), 3.55; 95% confidence interval (95% CI), 1.33-9.51; neighborhood-level education OR, 3.21; 95% CI, 1.25-8.26; White women: SEP Index OR, 2.13; 95% CI, 0.97-4.67; neighborhood-level education OR, 2.31; 95% CI, 0.93-5.76]. The results of this analysis underscore the importance of examining neighborhood social context as well as individual factors in the study of mammography screening behavior.

Disease and Disadvantage in the United States and in England

The United States spends considerably more money on health care than the United Kingdom, but whether that translates to better health outcomes is unknown. To assess the relative heath status of older individuals in England and the United States, especially how their health status varies by important indicators of socioeconomic position. We analyzed representative samples of residents aged 55 to 64 years from both countries using 2002 data from the US Health and Retirement Survey (n = 4386) and the English Longitudinal Study of Aging (n = 3681), which were designed to have directly comparable measures of health, income, and education. This analysis is supplemented by samples of those aged 40 to 70 years from the 1999-2002 waves of National Health and Nutrition Examination Survey (n = 2097) and the 2003 wave of the Health Survey for England (n = 5526). These surveys contain extensive and comparable biological disease markers on respondents, which are used to determine whether differential propensities to report illness can explain these health differences. To ensure that health differences are not solely due to health issues in the black or Latino populations in the United States, the analysis is limited to non-Hispanic whites in both countries. Self-reported prevalence rates of several chronic diseases related to diabetes and heart disease, adjusted for age and health behavior risk factors, were compared between the 2 countries and across education and income classes within each country. The US population in late middle age is less healthy than the equivalent British population for diabetes, hypertension, heart disease, myocardial infarction, stroke, lung disease, and cancer. Within each country, there exists a pronounced negative socioeconomic status (SES) gradient with self-reported disease so that health disparities are largest at the bottom of the education or income variants of the SES hierarchy. This conclusion is generally robust to control for a standard set of behavioral risk factors, including smoking, overweight, obesity, and alcohol drinking, which explain very little of these health differences. These differences between countries or across SES groups within each country are not due to biases in self-reported disease because biological markers of disease exhibit exactly the same patterns. To illustrate, among those aged 55 to 64 years, diabetes prevalence is twice as high in the United States and only one fifth of this difference can be explained by a common set of risk factors. Similarly, among middle-aged adults, mean levels of C-reactive protein are 20% higher in the United States compared with England and mean high-density lipoprotein cholesterol levels are 14% lower. These differences are not solely driven by the bottom of the SES distribution. In many diseases, the top of the SES distribution is less healthy in the United States as well. Based on self-reported illnesses and biological markers of disease, US residents are much less healthy than their English counterparts and these differences exist at all points of the SES distribution.

QL1: DOES SOCIOECONOMIC STATUS AFFECT THE VALUATION OF HEALTH?

OBJECTIVE: The issue of whose values count in the evaluation of health interventions is central to decision-making in all health care systems. Within the health services research community there is a degree of consensus that population-based preference weights should be used as the quality-adjustment factor in determining the value of health outcomes. However, previous research indicates an inverse, graded relationship between socioeconomic status (SES) and an individual's own health. If SES is also found to influence the valuation of hypothetical health states, then this could prove to have significant consequences for the evaluation of treatment. METHODS: Values for hypothetical health states defined by EQ-5D (a generic measure of health-related quality of life) were collected from a representative survey of the UK general population. 2,997 individuals used time trade-off (TTO) methods to value these EQ-5D health states. Information on each respondent included age, gender, social class and educational attainment. TTO values were bounded and non-normally distributed necessitating methods such as ordered logistic regression in addition to OLS to analyze these data. RESULTS: Education and social class as proxies for SES were significant predictors of the mean values for hypothetical health states. Their influence on health state valuation appears to act through their interaction with the mobility and self-care dimensions of the EQ-5D. This relationship persists after adjustment for respondent demographic characteristics. CONCLUSIONS: Valuation of hypothetical health states appears related to SES. This has implications for cost-effectiveness analysis since valuations from one population with a particular SES distribution may not be applicable for health policy and medical decision-making in other populations. The relationships between SES and health state valuation merit further investigation, in particular to examine the impact of non-health consequences such as income on values.

Socio-economic mobility among patients with schizophrenia or major affective disorder. A 17-year retrospective follow-up.

Social mobility among patients with schizophrenia or major affective disorder was compared with that among the general population. Mobility was studied retrospectively from 1970 to 1987. Socio-economic status (SES) was defined by occupation as in the population census (upper white-collar, lower white-collar, blue-collar, entrepreneur, farmer, unemployed). All patients aged 30-60 years at discharge (2901 men and 3620 women) in 1987-88 in Finland were included in the study. The SES structure of the general population was used for comparisons. Among patients with schizophrenia there was a constant downward drift, commonly to unemployment. This risk was higher among men than women. In the youngest age group a marked decline from the parents' social status was observed. Among patients with major affective disorder the distribution of SES in 1970 was similar to that of the general population. By 1987, a downward drift was again observed, mainly to unemployment regardless of the initial SES group. The number of patients in occupational categories were usually 30-50% lower than expected. Schizophrenic patients had a high risk of social drop-out. Among patients with major affective disorder the downward drift was much less.

Infantile autism and parental socioeconomic status: a case of bimodal distribution.

Kanner, in his 1943 article, reported that parents of autistic children tended to be of high socioeconomic status (SES). According to Schopler, Andrews and Strupp (1979), subsequent studies did not reveal similar findings. However, a careful analysis of these studies will show that in all instances the SES distributions of the parents were bimodal. The evidence shows that when Kanner's strict criteria are used, parents of autistic children are of higher SES. However, when children withautistic features are included in the first group, bimodal distributions are obtained. It is suggested that the question of upper SES of parents of autistic children is still open.

Race, Socioeconomic Status, and Other Prognostic Factors for Survival From Prostate Cancer<xref ref-type="fn" rid="FN2">2</xref><xref ref-type="fn" rid="FN3">3</xref>

Survival data on prostate cancer patients from 11 Comprehensive Cancer Centers contributing data to the Centralized Cancer Patient Data System were analyzed to examine the contribution of various factors to the probability of survival from prostate carcinoma. Application of a number of exclusion criteria resulted in a series of 2,513 patients (1,032 blacks and 1,481 Caucasians) for whom complete data on variables of interest were available. The stage of disease at diagnosis was a major determinant of survival. The proportion of blacks presenting the disease in advanced stage was substantially higher than that of Caucasians--a difference which was maintained within each socio-economic status (SES) category. Caucasian patients had a better prognosis than blacks for each disease stage. A dose-response relationship between SES and survival prognosis was observed and this relationship persisted for each stage of the disease. Although both race and SES turned out to be significant in regression models in which one or the other was considered, the model including both race and SES showed only SES to be a significant factor. Hence it can be hypothesized that the racial difference in the survival prognosis for prostate cancer is, to a large extent due to the differences in the distribution of SES in the two races.

A Comment on the Neonatal Mortality-Socioeconomic Status Relationship

Data for Toledo Ohio and San Antonio Texas reveal that a decline in neonatal mortality has led to a slight narrowing of the overall inverse relationship between infant mortality and socioeconomic status (SES) in both cities. At the same time there are notable differences in both the magnitude and pattern of the neonatal changes. In San Antonio the decline has led to a virtually nonexistent association between SES and neonatal mortality. In Toledo however although there has clearly been a convergence of neonatal mortality levels among 3 higher neighborhood groupings an inverse association is nevertheless still apparent and the lower SES area continues to be characterized by a neonatal death rate at least 40% higher than that of any other area. It is suggested that it is premature to infer as Markides and McFarland done (in an earlier issue) that those factors contributing to the neonatal mortality decline have affected lower SES infants to a greater extent. In evaluating the data presented it is stressed that the comparability of the 2 studies is limited: the 2 cities differ in size; they are located in markedly different regions; the dates covered are not identical; and they radically different ethnic compositions. Further both studies are limited by being based on aggregate rather than individual data; and reflecting the prolonged delay in releasing the 1980 census data neither study has been able to update its SES measures. In both studies 1970 income data were used to identify the SES areas for both time periods examined; and if there been any marked changes in the SES distribution of the population within either city since 1970 it could distort the nature and magnitude of the infant mortality-SES relationships.