Stephanie A. Robert

Community-level socioeconomic status effects on adult health

Do the socioeconomic characteristics of a community affect ones health? This research examines whether the socioeconomic characteristics of communities are associated with the health of community residents, over and above the socio-economic characteristics of individual residents and their families. This is the first study to examine the independent associations between community-level socio-economic status (SES) and individual-level health using a nationally representative sample of adults in the United States. Results indicate that a persons health is associated with SES characteristics of the community over and above ones own income, education, and assets. However, individual-level and family-level SES indicators are stronger predictors of health than community-level SES indicators. This research suggests that improving individual-level and family-level socioeconomic circumstances may be the more direct way to improve the health of individuals, but that understanding the community context in which a person lives may also ultimately be important to improving health.

SES Differentials in Health by Age and Alternative Indicators of SES

Despite the general persistence and even increase of strong socioeconomic status (SES) differentials in health in the United States, research suggests that SES differentials in health may diminish or become nonexistent at older ages. However, most research has used only limited measures of SES (e.g. education, income), and has not thoroughly investigated intra-elderly age differences in this trend. The current study investigates how SES differentials in health vary by age in the United States, using fairly detailed age categories (through ages 85+), and 2 alternative indicators (home ownership and liquid assets) of a major additional dimension of SES, financial assets, which may be especially important at older ages. We address (a) how strongly financial assets are associated with health, considered both alone and net of education and income; (b) if the health effects of financial assets vary by age; and, more specifically, (c) if their effects are especially pronounced in older age, again considered both alone and net of or relative to education and income. Results show that financial assets, especially liquid assets, considered both alone and net of education and income, are associated with health throughout adulthood and old age, at least until ages 85+. Furthermore, financial assets remain associated with health until quite late in life and become more important relative to education and income at older ages for some measures of health.

Socioeconomic risk factors for breast cancer: distinguishing individual- and community-level effects.

Background: Women are at higher risk of breast cancer if they have higher socioeconomic status (SES) or live in higher SES or urban communities. We examined whether women living in such communities remained at greater risk of breast cancer after controlling for individual education and other known individual-level risk factors. Methods: Data were from a population-based, breast cancer case-control study conducted in Wisconsin from 1988 to 1995 (n = 14,667). Data on community SES and urbanicity come from the 1990 census, measured at the census tract and zip code levels. We evaluated relationships between individual- and community-level variables and breast cancer risk using multilevel logistic regression models with random community intercepts. Results: After controlling for individual education and other individual-level risk factors (age, mammography use, family history of breast cancer, parity, age at first birth, alcohol intake, body mass index, hormone replacement use, oral contraceptive use, and menopausal status), women living in the highest SES communities had greater odds of having breast cancer than women living in the lowest SES communities (1.20; 95% confidence interval = 1.05–1.37). Similarly, the odds were greater for women in urban versus rural communities (1.17; 1.06–1.28). Conclusions: Community SES and urbanicity are apparently not simply proxies for individual SES. Future research should examine why living in such communities itself is associated with greater risk of breast cancer.

Subjective and objective neighborhood characteristics and adult health

This study examines both objective and subjective assessments of neighborhood conditions, exploring the overlap between different sources of information on neighborhoods and the relative strength of their association with adult self-rated health. Data on perceived neighborhood quality from Wave IV (2001/2002) of the nationally representative U.S. Americans Changing Lives study are merged with neighborhood-level census tract data to measure subjective and objective neighborhood constructs. Structural equation models indicate that subjective and objective constructs are both related to health. However, the subjective construct (perceived neighborhood quality) is most strongly associated with health and mediates associations between health and the objective constructs (neighborhood disadvantage and affluence). Additionally, individual characteristics play an important role in shaping the contribution of neighborhood conditions through selection and mediation. Our results demonstrate the independent associations between both objective and perceived neighborhood quality and health, and highlight the particularly strong association between perceived neighborhood quality and health.

Geocoding addresses from a large population-based study: lessons learned.

Background Geographic information systems (GIS) and spatial statistics are useful for exploring the relation between geographic location and health. The ultimate usefulness of GIS depends on both completeness and accuracy of geocoding (the process of assigning study participants’ residences latitude/longitude coordinates that closely approximate their true locations, also known as address matching). The goal of this project was to develop an iterative geocoding process that would achieve a high match rate in a large population-based health study. Methods Data were from a study conducted in Wisconsin using mailing addresses of participants who were interviewed by telephone from 1988 to 1995. We standardized the addresses according to US Postal Service guidelines, used desktop GIS geocoding software and two versions of the Topologically Integrated Geographic Encoding and Referencing street maps, accessed Internet mapping engines for problematic addresses, and recontacted a small number of study participants’ households. We also tabulated the project’s cost, time commitment, software requirements, and brief notes for each step and their alternatives. Results Of the 14,804 participants, 97% were ultimately assigned latitude/longitude coordinates corresponding to their respective residences. The remaining 3% were geocoded to their zip code centroid. Conclusion The multiple methods described in this work provide practical information for investigators who are considering the use of GIS in their population health research.

Gender differences in health-related quality-of-life are partly explained by sociodemographic and socioeconomic variation between adult men and women in the US: evidence from four US nationally representative data sets

PurposeThe purpose of this study was to describe gender differences in self-reported health-related quality-of-life (HRQoL) and to examine whether differences are explained by sociodemographic and socioeconomic status (SES) differentials between men and women.MethodsData were from four US nationally representative surveys: US Valuation of the EuroQol EQ-5D Health States Survey (USVEQ), Medical Expenditure Panel Survey (MEPS), National Health Measurement Study (NHMS) and Joint Canada/US Survey of Health (JCUSH). Gender differences were estimated with and without adjustment for sociodemographic and SES indicators using regression within and across data sets with SF-6D, EQ-5D, HUI2, HUI3 and QWB-SA scores as outcomes.ResultsWomen have lower HRQoL scores than men on all indexes prior to adjustment. Adjusting for age, race, marital status, education and income reduced but did not remove the gender differences, except with HUI3. Adjusting for marital status or income had the largest impact on estimated gender differences.ConclusionsThere are clear gender differences in HRQoL in the United States. These differences are partly explained by sociodemographic and SES differentials.

Message design strategies to raise public awareness of social determinants of health and population health disparities.

CONTEXT Raising public awareness of the importance of social determinants of health (SDH) and health disparities presents formidable communication challenges. METHODS This article reviews three message strategies that could be used to raise awareness of SDH and health disparities: message framing, narratives, and visual imagery. FINDINGS Although few studies have directly tested message strategies for raising awareness of SDH and health disparities, the accumulated evidence from other domains suggests that population health advocates should frame messages to acknowledge a role for individual decisions about behavior but emphasize SDH. These messages might use narratives to provide examples of individuals facing structural barriers (unsafe working conditions, neighborhood safety concerns, lack of civic opportunities) in efforts to avoid poverty, unemployment, racial discrimination, and other social determinants. Evocative visual images that invite generalizations, suggest causal interpretations, highlight contrasts, and create analogies could accompany these narratives. These narratives and images should not distract attention from SDH and population health disparities, activate negative stereotypes, or provoke counterproductive emotional responses directed at the source of the message. CONCLUSIONS The field of communication science offers valuable insights into ways that population health advocates and researchers might develop better messages to shape public opinion and debate about the social conditions that shape the health and well-being of populations. The time has arrived to begin thinking systematically about issues in communicating about SDH and health disparities. This article offers a broad framework for these efforts and concludes with an agenda for future research to refine message strategies to raise awareness of SDH and health disparities.

Socioeconomic status and survival after an invasive breast cancer diagnosis.

Women who live in geographic areas with high poverty rates and low levels of education experience poorer survival after a breast cancer diagnosis than women who live in communities with indicators of high socioeconomic status (SES). However, very few studies have examined individual‐level SES in relation to breast cancer survival or have assessed the contextual role of community‐level SES independent of individual‐level SES.

Age Variation in the Relationship Between Community Socioeconomic Status and Adult Health

Research demonstrating that socioeconomic status (SES) differentials in health are smaller at older ages often considers only individual SES measures (e.g., income, education) but not community SES measures (e.g., community poverty rate), although the gerontological literature suggests that community context may be particularly salient in the lives of older adults. This study uses two national surveys of adults, each matched with census data about respondents’ communities, to examine whether the association between community SES and individual health is stronger at consecutively older age groups. The association between community SES and health is nonexistent or weak during younger adulthood, stronger through middle ages, strongest at ages 60 to 69, and weak again at ages 70 and older. At ages 60 to 69, community SES effects are stronger than or comparable to individual SES effects. Community SES should be considered an important dimension of SES when exploring the impact of SES on health over the life course.

Excess mortality among urban residents: how much, for whom, and why?

OBJECTIVES The goals of this study were to estimate prospective mortality risks of city residence, specify how these risks vary by population subgroup, and explore possible explanations. METHODS Data were derived from a probability sample of 3617 adults in the coterminous United States and analyzed via cross-tabular and Cox proportional hazards methods. RESULTS After adjustment for baseline sociodemographic and health variables, city residents had a mortality hazard rate ratio of 1.62 (95% confidence interval [CI] = 1.21, 2.18) relative to rural/small-town residents; suburbanites had an intermediate but not significantly elevated hazard rate ratio. This urban mortality risk was significant among men (hazard rate ratio: 2.25), especially non-Black men, but not among women. Among Black men, and to some degree Black women, suburban residence carried the greatest risk. All risks were most evident for those younger than 65 years. CONCLUSIONS The mortality risk of city residence, at least among men, rivals that of major psychosocial risk factors such as race, low income, smoking, and social isolation and merits comparable attention in research and policy.