Area-based Socioeconomic Measures Research Articles

Association of cutaneous melanoma incidence with area-based socioeconomic indicators–United States, 2004-2006

Socioeconomic status (SES) has been associated with melanoma incidence and outcomes. Examination of the relationship between melanoma and SES at the national level in the United States is limited. Expanding knowledge of this association is needed to improve early detection and eliminate disparities. We sought to provide a detailed description of cutaneous melanoma incidence and stage of disease in relationship to area-based socioeconomic measures including poverty level, education, income, and unemployment in the United States. Invasive cutaneous melanoma data reported by 44 population-based central cancer registries for 2004 to 2006 were merged with county-level SES estimates from the US Census Bureau. Age-adjusted incidence rates were calculated by gender, race/ethnicity, poverty, education, income, unemployment, and metro/urban/rural status using software. Poisson multilevel mixed models were fitted, and incidence density ratios were calculated by stage for area-based SES measures, controlling for age, gender, and state random effects. Counties with lower poverty, higher education, higher income, and lower unemployment had higher age-adjusted melanoma incidence rates for both early and late stage. In multivariate models, SES effects persisted for early-stage but not late-stage melanoma incidence. Individual-level measures of SES were unavailable, and estimates were based on county-level SES measures. Our findings show that melanoma incidence in the United States is associated with aggregate county-level measures of high SES. Analyses using finer-level SES measures, such as individual or census tract level, are needed to provide more precise estimates of these associations.

Socioeconomic Factors and Diagnosis of Uveal Melanoma in the Mid-Southern United States

Purpose: To establish the base incidence of uveal melanoma in the mid-southern United States and to explore the regional frequency of uveal melanoma diagnosis as a function of area-based socioeconomic measures (ABSM) aggregated at the level of small geographic units delimited by Zoning Improvement Plan (ZIP) codes.Methods: Based on a retrospective chart review (1996–2007) of patients seen at our institutions with the diagnosis of uveal melanoma, the number of uveal melanoma cases was calculated for each ZIP-code in Arkansas, Mississippi, western Tennessee and Louisiana. The base incidence of uveal melanoma was calculated using the population size reported in the 2000 census as the population at risk for each geographic area. Data on the average house value and average household income reported in the 2000 census were used in a Poisson regression analysis to examine their effect on the frequency of uveal melanoma diagnosis.Results: There were 327 (of 1,669) regional ZIP-codes that were the source of 457 patients with uveal melanoma. Higher ABSM, defined as greater average house value or household income, were positively associated with the number of observed melanoma cases per ZIP-code. The annualized incidence of uveal melanoma was at least 3.5 cases per million in the areas studied.Conclusion: Higher ABSM were associated with the increased frequency of uveal melanoma diagnosis in the regions studied. Extrapolating from similar trends observed with non-ocular cancers, this may signify a need for increased access to ophthalmologic care to ensure timely diagnosis.

O2-S3.06 Area-based socioeconomic measures associated with female chlamydia and gonorrhoea, San Francisco, 2009

BackgroundSTD risk is influenced by individual-, network-, and community-level factors. Most research on STD risk has focused on individual-level factors, while limited data are available on community-level influences. In an...

Assessing Coronary Risk Assessment: What's Next?

Assessing Coronary Risk Assessment: What's Next?

Monitoring Health Inequities and Planning in Virginia: Poverty, Human Immunodeficiency Virus, and Sexually Transmitted Infections

Monitoring social inequalities in sexual health is important to the effective allocation of resources for human immunodeficiency virus (HIV) and sexually transmitted infection (STI) prevention by state health departments and other outside planning groups. At the Virginia Department of Health, like most US public health agencies, there is a lack of consistent socioeconomic data, such as individual-level poverty measures, collected through routine disease surveillance.1 As a result, state epidemiologists are only able to provide a general description of poverty, such as the percentage of people living in poverty while providing HIV/STI rates for the same administrative area.2–8 This article has 2 objectives. The first is to quantify and compare HIV/STI incidence across 4 stratum of poverty at the census tract level in Virginia using methods developed by the Public Health Disparities Geocoding Project.9 Second, for each poverty strata we examine the distribution of HIV-positive and other high-risk populations, identified as priority concerns for HIV prevention planning in Virginia (“priority populations”). This research can help epidemiologists and outside planning groups, who often lack access to geocoded data guide resource allocation for HIV/STI prevention. The Public Health Disparities Geocoding Project helps facilitate routine monitoring of health inequities in the United States by providing epidemiologists with an established statistical framework for comparing disease rates at the census tract level to a census tract-based measure of poverty.9 The methodology for monitoring socioeconomic inequalities through quantifying the relationship between infection rates and area-based socioeconomic measures (ABSM) was developed and is described by the Public Health Disparities Geocoding Project. Briefly, outcomes were geocoded to the census tract level, tracts were stratified into discrete poverty levels (0%– 4.9%, 5.0%–9.9%, 10.0%–19.99%, and 20%–100% population living below poverty), age-standardized incidence rates were calculated for each stratum of poverty, and 95% confidence intervals based on the distribution were calculated.9 To calculate infection rates at the census tract level, we geocoded clinically diagnosed cases of HIV [regardless of transition to acquired immune deficiency syndrome (AIDS)], chlamydia (CT), gonorrhea (NG), and total early syphilis (TES) between 2000 and 2005 using addresses obtained from Virginia’s HIV and AIDS Reporting System (HARS) and the STD Management Information System (STDMIS). Geocoding was performed with Centrus Geostan.10 Each census tract in Virginia was stratified into 1 of 4 discrete poverty stratum, which are defined by an area-based socioeconomic measure created by the Public Health Disparities Geocoding Project from 2000 census data.11 Each stratum is an aggregation of census tracts based on the percentage of the tract’s total population living below the federal poverty line. Four hundred eighty (31%), 428 (28%), 418 (27%), and 206 (13%) census tracts were assigned to the 0% to 4.9%, 5.0% to 9.9%, 10.0% to 19.9%, and 20.0% to 100% stratum, respectively. Age-standardized incidence rates and incidence rate ratios (IRRs) for each disease were calculated for each stratum of poverty, and 95% confidence intervals based on the distribution were calculated. Gamma intervals are commonly used when the outcomes are directly standardized rates and a small number of cases and a large variability in weights exists.12 Addresses were considered geocodable to the census tract level if they geocoded to a street/house/intersection, the center of a block group/census tract or the center of a zip code, where The authors wish to thank Rene Cabral-Daniels, Jason Carr, Jeanette Gustat, Hongjie Liu and Jeff Stover, for their careful review of the manuscript. This activity was conducted, in part, through the support of the Centers for Disease Control and Prevention (CDC) Cooperative Agreement No. U62-PS000559, “Evaluating Integration of HIV/AIDS Surveillance Data with a Geographic Information System.” Additional support was provided through ongoing collaborative activities associated with the Outcomes Assessment through Systems of Integrated Surveillance (OASIS) workgroup, which was a CDC funded activity from 1998 through 2005. The Health Informatics and Integrated Surveillance Systems staff within the Virginia Department of Health-Division of Disease Prevention are grateful for the impetus OASIS had on our geographic information systems initiatives, our analytical infrastructure and our public health network of colleagues. Correspondence: Carrie Dolan, MPH, 5308 Discovery Park Building, Suite 101, Williamsburg, VA 23188. E-mail: cdolan@wchf.com. Received for publication August 30, 2007, and accepted June 7, 2008. From the Division of Disease Prevention, Virginia Department of Health, Richmond, Virginia Sexually Transmitted Diseases, December 2008, Vol. 35, No. 12, p.981–984 DOI: 10.1097/OLQ.0b013e318182a571 Copyright © 2008, American Sexually Transmitted Diseases Association All rights reserved.

The effects of socioeconomic status and indices of physical environment on reduced birth weight and preterm births in Eastern Massachusetts.

BackgroundAir pollution and social characteristics have been shown to affect indicators of health. While use of spatial methods to estimate exposure to air pollution has increased the power to detect effects, questions have been raised about potential for confounding by social factors.MethodsA study of singleton births in Eastern Massachusetts was conducted between 1996 and 2002 to examine the association between indicators of traffic, land use, individual and area-based socioeconomic measures (SEM), and birth outcomes (birth weight, small for gestational age and preterm births), in a two-level hierarchical model.ResultsWe found effects of both individual (education, race, prenatal care index) and area-based (median household income) SEM with all birth outcomes. The associations for traffic and land use variables were mainly seen with birth weight, with an exception for an effect of cumulative traffic density on small for gestational age. Race/ethnicity of mother was an important predictor of birth outcomes and a strong confounder for both area-based SEM and indices of physical environment. The effects of traffic and land use differed by level of education and median household income.ConclusionOverall, the findings of the study suggested greater likelihood of reduced birth weight and preterm births among the more socially disadvantaged, and a greater risk of reduced birth weight associated with traffic exposures. Results revealed the importance of controlling simultaneously for SEM and environmental exposures as the way to better understand determinants of health.

Colorectal cancer stage at diagnosis and area socioeconomic characteristics in New Jersey

Despite effective screening methods, research suggests consistently higher rates of late stage colorectal cancer (CRC) among persons living in low socioeconomic areas compared to those living in affluent areas. This population-based study evaluated the association between area-based socioeconomic measures (ABSMs) and CRC stage at diagnosis in New Jersey. Cases of CRC among persons 50 years and older, diagnosed from 2000–2005, were obtained from the New Jersey State Cancer Registry. Associations between census tract-level ABSMs and CRC stage at diagnosis were evaluated using logistic regression and geographic variation assessed using a spatial scan statistic. After adjusting for covariates, including individual-level health insurance, ABSMs were significantly associated with stage at diagnosis. As area socioeconomic conditions worsened, the odds of being diagnosed at a late stage increased. While increasing CRC screening services for all New Jersey populations is warranted, this study suggests that persons living in low socioeconomic areas could benefit the most from enhanced CRC education, screening efforts, and guided interventions.

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Comparing Individual- and Area-based Socioeconomic Measures for the Surveillance of Health Disparities: A Multilevel Analysis of Massachusetts Births, 1989–1991

The absence of individual-level socioeconomic information in most US health surveillance data necessitates using area-based socioeconomic measures (ABSMs) to monitor health inequalities. Using the 1989-1991 birth weight data from Massachusetts, the authors compared estimates of health disparities detected with census tract- and block group-level ABSMs pertaining to poverty and education, as well as parental education, both independently and together. In separate models, adjusted for infant's sex, mother's age, and parents' race/ethnicity, worst-off categories of census tract ABSMs and parental education had a comparable birth weight deficit of approximately 70 g. Similar results were observed for low birth weight (<2,500 g), with worst-off categories of census tract ABSMs and parental education having an odds ratio of approximately 1.37 (p < 0.001). In mutually adjusted models for birth weight and low birth weight, census tract ABSMs still detected an effect estimate nearly 50% of that detected by parental education. Additionally, census tract ABSMs detected socioeconomic gradients in birth weight among births to mothers aged less than 25 years, an age group in which educational attainment is unlikely to be completed. These results suggest that aptly chosen ABSMs can be used to monitor socioeconomic inequalities in health. The risk, if any, in the absence of individual-level socioeconomic information is a conservative estimate of socioeconomic inequalities in health.

Invited Commentary: Using Area-based Socioeconomic Measures—Think Conceptually, Act Cautiously

Invited Commentary: Using Area-based Socioeconomic Measures—Think Conceptually, Act Cautiously

Monitoring Socioeconomic Disparities in Death: Comparing Individual-Level Education and Area-Based Socioeconomic Measures

We compared all-cause mortality rates stratified by individual-level education and by census tract area-based socioeconomic measures for Massachusetts (1999-2001). Among persons aged 25 and older, the age-adjusted relative index of inequality was slightly higher for the census tract than for the individual education measures (1.5 vs 1.2, respectively). Only the census tract socioeconomic measures could provide a relative index of inequality (2-3) for deaths before age 25 or detect expected socioeconomic disparities for deaths among persons 65 and older (relative index of inequality= approximately 1.2 vs 0.8 for census tract measures and individual education, respectively).

Race/Ethnicity and Changing US Socioeconomic Gradients in Breast Cancer Incidence: California and Massachusetts, 1978–2002 (United States)

We tested the hypothesis that the US socioeconomic gradient in breast cancer incidence is declining, with the decline most pronounced among racial/ethnic groups with the highest incidence rates. We geocoded the invasive incident breast cancer cases for three US population-based cancer registries covering: Los Angeles County, CA (1978-1982, 1988-1992, 1998-2002; n = 68,762 cases), the San Francisco Bay Area, CA (1978-1982, 1988-1992, 1998-2002; n = 37,210 cases) and Massachusetts (1988-1992, 1998-2002; n = 48,111 cases), linked the records to census tract area-based socioeconomic measures, and, for each socioeconomic stratum, computed average annual breast cancer incidence rates for the 5-year period straddling the 1980, 1990, and 2000 census, overall and by race/ethnicity and gender. Our findings indicate that the socioeconomic gradient in breast cancer incidence is: (a) relatively small (at most 1.2) and stable among US white non-Hispanic and black women; (b) sharper and generally increasing among Hispanic and Asian and Pacific Islander American women; and (c) cannot be meaningfully analyzed without considering effect modification by race/ethnicity and immigration. Our results indicate that secular changes in US socioeconomic gradients in breast cancer incidence exist and vary by race/ethnicity.

The usefulness of area-based socioeconomic measures to monitor social inequalities in health in Southern Europe

The study objective was to investigate the association between health outcomes and several small-area-based socioeconomic measures and also with individual socioeconomic measures as a check on external validity. Cross-sectional design based on the analysis of the Barcelona Health Interview Survey of 1992. A representative stratified sample of the non-institutionalised population resident in Barcelona city (Spain) was obtained. The present study refers to the 4171 respondents aged over 14. We studied perceived health status, presence of chronic conditions and smoking as health outcomes. Area socioeconomic measures (1991 census) were generated at census tract level and individual socioeconomic measures were educational level and social class obtained through the survey. With individual socioeconomic measures we observed that the lower the educational level or social class, the higher the probability of reporting a perceived health status of fair, poor or very poor and of presenting some chronic condition. With regard to smoking, among men this trend was similar [odds ratio (OR) = 1.5; 95% confidence interval (CI) = 1.2-1.9 in social classes IV-V with respect to social classes I-II], while among women it was reversed (OR = 0.7; 95% CI = 0.5-0.9). With the different area-based socioeconomic indicators differences were also observed in this sense, with the exception of smoking in women for which these indicators do not show any differences by socioeconomic level. With several census area-based socioeconomic measures similar effects on inequalities in health have been observed. In general, these inequalities were in the same sense as those obtained with individual-based measures. Small-area-based socioeconomic measures from the Spanish census could greatly enhance analysis of social inequalities in health, overcoming the absence of socioeconomic data in public health registries and in medical records.

Socio‐economic inequalities in the prevalence of Type 2 diabetes, cardiovascular risk factors and chronic diabetic complications in the Basque Country, Spain

To establish the relationship between socio-economic status and the prevalence of known Type 2 diabetes, cardiovascular risk factors and chronic diabetic complications. In 2000, a cross-sectional survey was conducted among 61 general practitioners (GPs) who studied 65 651 people older than 24 years. Of those, 2985 known Type 2 diabetic patients were registered. The main outcome measures were: diabetes prevalence, major cardiovascular risk factors, chronic diabetic complications and primary care services utilization in Type 2 diabetic patients. Socio-economic status was based on area-based socio-economic measures. The prevalence of known Type 2 diabetes was higher in patients of lower socio-economic status (OR: 2.17, 95% CI: 1.77-2.28), especially among women (OR: 2.28, 95% CI: 1.91-2.73). In Type 2 diabetes patients, obesity, sedentary lifestyle, and abnormal levels of low-density lipoprotein (LDL) cholesterol and HbA(1c) were more prevalent among those from lower socio-economic status. Macroangiopathy was inversely associated with socio-economic status after adjustment for clinical and demographic variables. Patients of lower socio-economic status more frequently visited primary care services than those of higher status. This study shows an association between deprivation and Type 2 diabetes prevalence, cardiovascular risk factors and chronic diabetic complications in Type 2 diabetes patients. Despite a greater use of health services by less wealthy patients, they showed worse glycaemic control and more chronic complications. Besides clinical variables, socio-economic status and environmental information need to be considered in the assessment of risk profile of diabetic patients by health professionals and by health service planners.

Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures--the public health disparities geocoding project.

Use of multilevel frameworks and area-based socioeconomic measures (ABSMs) for public health monitoring can potentially overcome the absence of socioeconomic data in most US public health surveillance systems. To assess whether ABSMs can meaningfully be used for diverse race/ethnicity-gender groups, we geocoded and linked public health surveillance data from Massachusetts and Rhode Island to 1990 block group, tract, and zip code ABSMs. Outcomes comprised death, birth, cancer incidence, tuberculosis, sexually transmitted infections, childhood lead poisoning, and nonfatal weapons-related injuries. Among White, Black, and Hispanic women and men, measures of economic deprivation (e.g., percentage below poverty) were most sensitive to expected socioeconomic gradients in health, with the most consistent results and maximal geocoding linkage evident for tract-level analyses.

KRIEGER ET AL. RESPOND

As noted by Caretta et al, researchers should be cognizant of diverse types of spatiotemporal discontinuities that may affect the use of geographically delimited population data. Their letter, while alluding to—but never identifying any—minor factual errors in our brief, gives us an opportunity to address confusion about the use of zip codes (ZCs), zip code tabulation areas (ZCTAs), and census-derived area-based socioeconomic measures (ABSMs). We documented a type of bias specifically affecting ZC-linked ABSMs, resulting from changes in ZCs and boundaries between decennial censuses, a complication not affecting census tracts (CTs) or block groups (BGs). At issue is the use of ZC-level ABSMs to characterize the socioeconomic context in which individuals reside, whether for population-based incidence studies, case–control studies, randomized clinical trials, or other study designs. By suggesting that ZC data are no more or less problematic than CT or BG data, the letter by Caretta et al is misleading on 3 counts: Although CT boundaries can change from one census to the next, methods exist to compare CTs over time that cannot be used for ZCs.1,2 For example, CTs split in 2000 can be consolidated to the 1990 tract boundaries; CTs merged in 2000 can be disaggregated to BGs to reconstitute the 1990 CT boundaries; and “supertracts” can be created, incorporating several 1990 CTs or BGs to provide a match to a similar combination of 2000 CTs or BGs.2 These methods cannot be employed with ZCs, precisely because they lack stable census-defined boundaries. Even though a relatively small percentage of ZCs may cross over state lines, the point is that this phenomenon does occur; by contrast, it cannot occur with CTs or BGs.3 The stability of ZCTAs is the same as that of CTs, rendering the suggested positive feature of ZCs’ reflecting population change more quickly than CTs irrelevant to ZCTAs. More important, as we emphasized in our brief, the US Census Bureau has explicitly warned that ZCs cannot be directly matched to ZCTAs: ZCs and ZCTAs having the same numerical code can encompass different areas. Geocoding is thus required to ascertain an address’s ZCTA, thereby removing one often-cited advantage of using postal ZCs. One major implication is that design and use of administrative databases that currently obtain or provide only the ZC for linkage to census-derived ABSMs will need to be reconsidered, given the lack of congruence between ZCs and ZCTAs.

Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measures--the public health disparities geocoding project (US).

To determine which area-based socioeconomic measures, at which level of geography, are suitable for monitoring socioeconomic inequalities in sexually transmitted infections (STIs), tuberculosis (TB), and violence in the United States. Cross-sectional analysis of public health surveillance data, geocoded and linked to area-based socioeconomic measures generated from 1990 census tract, block group, and ZIP Code data. We included all incident cases among residents of either Massachusetts (MA; 1990 population = 6016425) or Rhode Island (RI; 1990 population = 1003464) for: STIs (MA: 1994-1998, n = 26535 chlamydia, 7464 gonorrhea, 2619 syphilis; RI: 1994-1996, n = 4473 chlamydia, 1256 gonorrhea, 305 syphilis); TB (MA: 1993-1998, n = 1793; RI: 1985-1994, n = 576), and non-fatal weapons related injuries (MA: 1995-1997, n = 6628). Analyses indicated that: (a). block group and tract socioeconomic measures performed similarly within and across both states, with results more variable for the ZIP Code level measures; (b). measures of economic deprivation consistently detected the steepest socioeconomic gradients, considered across all outcomes (incidence rate ratios on the order of 10 or higher for syphilis, gonorrhea, and non-fatal intentional weapons-related injuries, and 7 or higher for chlamydia and TB); and (c). results were similar for categories generated by quintiles and by a priori categorical cut-points. Supplementing U.S. public health surveillance systems with census tract or block group area-based socioeconomic measures of economic deprivation could greatly enhance monitoring and analysis of social inequalities in health in the United States.

Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measures—the public health disparities Geocoding Project (US)

Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measures—the public health disparities Geocoding Project (US)

Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: does the choice of area-based measure and geographic level matter?: the Public Health Disparities Geocoding Project.

Despite the promise of geocoding and use of area-based socioeconomic measures to overcome the paucity of socioeconomic data in US public health surveillance systems, no consensus exists as to which measures should be used or at which level of geography. The authors generated diverse single-variable and composite area-based socioeconomic measures at the census tract, block group, and zip code level for Massachusetts (1990 population: 6,016,425) and Rhode Island (1990 population: 1,003,464) to investigate their associations with mortality rates (1989-1991: 156,366 resident deaths in Massachusetts and 27,291 in Rhode Island) and incidence of primary invasive cancer (1988-1992: 140,610 resident cases in Massachusetts; 1989-1992: 19,808 resident cases in Rhode Island). Analyses of all-cause and cause-specific mortality rates and all-cause and site-specific cancer incidence rates indicated that: 1) block group and tract socioeconomic measures performed comparably within and across both states, but zip code measures for several outcomes detected no gradients or gradients contrary to those observed with tract and block group measures; 2) similar gradients were detected with categories generated by quintiles and by a priori categorical cutpoints; and 3) measures including data on economic poverty were most robust and detected gradients that were unobserved using measures of only education and wealth.

Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project.

Zip code caveat: bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas--the Public Health Disparities Geocoding Project.