Socioeconomic Status Adjustment Research Articles

Abstract WMP46: Impact Of Poverty On Stroke Incidence And Recurrence: A Population-based Study

Background: Poorer socioeconomic status (SES) is associated with higher stroke incidence. Less is known about SES and stroke recurrence. We sought to obtain updated estimates of stroke incidence stratified by aggregate measures of SES, and to explore the association between SES and stroke recurrence. Methods: The Greater Cincinnati/Northern Kentucky region includes a population of 1.3 million, representative of the US population in terms of sociodemographics and percent black race. We ascertained all hospitalized strokes in the region in 2015 by screening ICD-9 codes 430-437 and ICD-10 codes I60-69, G45-46. Recurrent strokes were ascertained from 1/1/2015-12/31/2018. Patients’ home addresses were geocoded using DeGAUSS. Population estimates were obtained from the US Census Bureau using the 2015 5-year American Community Survey. Aggregate SES was estimated by percentage below poverty in each census tract. Regional incidence and recurrence rates were adjusted for age, sex, and race and calculated both with and without SES adjustment using Poisson regression models. Results: Stroke incidence and recurrence rates stratified by SES are shown in the Table. Poorer SES was associated with greater stroke incidence (p<0.01) and recurrence (p<0.01) across races. The relative risk (95% CI) for first-ever stroke among black compared with nonblack individuals was 2.06 (1.79-2.38) before adjusting for SES, and 1.79 (1.54-2.08) after adjusting for SES. The relative risk (95% CI) for recurrent stroke among black compared with nonblack individuals was 2.54 (1.91-3.37) before adjusting for SES, and 2.00 (1.47-2.74) after adjusting for SES. There was no race by SES interaction. Conclusions: Poorer SES was associated with increased risk for both incident and recurrent stroke across races. Of the excess risk for stroke incidence among black individuals, 25.5% was accounted for by SES, while 35.1% of the excess risk for recurrence was accounted for by SES.

Under capital’s thumb: longitudinal associations between relational social class and health

BackgroundWe used a relational social-class measure based on property ownership and managerial authority to analyse the longitudinal relationships between class, self-rated health (SRH) and mental illness. To our knowledge, this...

Impact of Risk Adjustment for Socioeconomic Status on Risk-adjusted Surgical Readmission Rates.

To assess whether differences in readmission rates between safety-net hospitals (SNH) and non-SNHs are due to differences in hospital quality, and to compare the results of hospital profiling with and without SES adjustment. In response to concerns that quality measures unfairly penalizes SNH, NQF recently recommended that performance measures adjust for socioeconomic status (SES) when SES is a risk factor for poor patient outcomes. Multivariate regression was used to examine the association between SNH status and 30-day readmission after major surgery. The results of hospital profiling with and without SES adjustment were compared using the CMS Hospital Compare and the Hospital Readmissions Reduction Program (HRRP) methodologies. Adjusting for patient risk and SES, patients admitted to SNHs were not more likely to be readmitted compared with patients in in non-SNHs (AOR 1.08; 95% CI:0.95-1.23; P = 0.23). The results of hospital profiling based on Hospital Compare were nearly identical with and without SES adjustment (ICC 0.99, κ 0.96). Using the HRRP threshold approach, 61% of SNHs were assigned to the penalty group versus 50% of non-SNHs. After adjusting for SES, 51% of SNHs were assigned to the penalty group. Differences in surgery readmissions between SNHs and non-SNHs are due to differences in the patient case mix of low-SES patients, and not due to differences in quality. Adjusting readmission measures for SES leads to changes in hospital ranking using the HRRP threshold approach, but not using the CMS Hospital Compare methodology. CMS should consider either adjusting for the effects of SES when calculating readmission thresholds for HRRP, or replace it with the approach used in Hospital Compare.

Racial-ethnic disparities in management and outcomes among children with type 1 diabetes.

Previous research has documented racial/ethnic disparities in diabetes treatments and outcomes. It remains controversial whether these disparities result from differences in socioeconomic status (SES) or other factors. We examined racial/ethnic disparities in therapeutic modalities and diabetes outcomes among the large number of pediatric participants in the T1D Exchange Clinic Registry. The cohort included 10 704 participants aged <18 years with type 1 diabetes for ≥1 year (48% female; mean age: 11.9 ± 3.6 years; diabetes duration: 5.2 ± 3.5 years). Diabetes management and clinical outcomes were compared among 8841 non-Hispanic white (white) (83%), 697 non-Hispanic black (black) (7%), and 1166 Hispanic (11%) participants. The population included 214 high-income black and Hispanic families. Insulin pump use was higher in white participants than in black or Hispanic participants (61% vs 26% and 39%, respectively) after adjusting for gender, age, diabetes duration, and SES (P < .001). Mean hemoglobin A1c was higher (adjusted P < .001) in black participants than in white or Hispanic participants (9.6%, 8.4%, and 8.7%). More black participants experienced diabetic ketoacidosis and severe hypoglycemic events in the previous year than white or Hispanic participants (both, P < .001). There were no significant differences in hemoglobin A1c, diabetic ketoacidosis, or severe hypoglycemia between white and Hispanic participants after adjustment for SES. Even after SES adjustment, marked disparities in insulin treatment method and treatment outcomes existed between black versus Hispanic and white children within this large pediatric cohort. Barriers to insulin pump use and optimal glycemic control beyond SES should be explored in all ethnic groups.

Financial hardship is associated with increased weight gain: what is the role of health behaviors? (811.25)

Weight status is strongly patterned by socioeconomic status (SES), but financial hardship may also separately influence weight. We aimed to examine the independent associations between financial hardship and 10‐year weight gain, and explore health behaviors as potential mechanisms, in British employees (n=3638). Self‐reported financial hardship (having difficulty paying bills and insufficient money for food/clothing) was assessed four times from 1985 to 1997 and weight measured twice (1985; 1997). During follow‐up, 2520 people gained 蠅5 kg, with a dose‐response to financial hardship in women only. Compared with no history of insufficient money for food/clothing, women reporting cumulative exposure had a 35% greater odds of gaining 蠅5 kg (1.00‐1.82) which increased after SES adjustment (OR 1.40; 1.01‐1.93). Change in smoking or marital status met mediation criteria, but only marital status altered women’s odds of weight gain from cumulative exposure to insufficient money (OR 1.54; 1.11‐2.14) or to difficulty paying bills (OR 1.44; 1.01‐2.06). Financial hardship was significantly independently related to subsequent weight gain in female British employees. One health behavior was a potential mediator but did not attenuate observed associations. Results suggest strategies to tackle obesity must address women’s current economic concerns which may influence weight through more biological pathways.Grant Funding Source: US National Heart Lung and Blood Institute R01HL036310; US National Institute on Aging R01AG01319

Measuring socioeconomic status in multicountry studies: results from the eight-country MAL-ED study

BackgroundThere is no standardized approach to comparing socioeconomic status (SES) across multiple sites in epidemiological studies. This is particularly problematic when cross-country comparisons are of interest. We sought to develop a simple measure of SES that would perform well across diverse, resource-limited settings.MethodsA cross-sectional study was conducted with 800 children aged 24 to 60 months across eight resource-limited settings. Parents were asked to respond to a household SES questionnaire, and the height of each child was measured. A statistical analysis was done in two phases. First, the best approach for selecting and weighting household assets as a proxy for wealth was identified. We compared four approaches to measuring wealth: maternal education, principal components analysis, Multidimensional Poverty Index, and a novel variable selection approach based on the use of random forests. Second, the selected wealth measure was combined with other relevant variables to form a more complete measure of household SES. We used child height-for-age Z-score (HAZ) as the outcome of interest.ResultsMean age of study children was 41 months, 52% were boys, and 42% were stunted. Using cross-validation, we found that random forests yielded the lowest prediction error when selecting assets as a measure of household wealth. The final SES index included access to improved water and sanitation, eight selected assets, maternal education, and household income (the WAMI index). A 25% difference in the WAMI index was positively associated with a difference of 0.38 standard deviations in HAZ (95% CI 0.22 to 0.55).ConclusionsStatistical learning methods such as random forests provide an alternative to principal components analysis in the development of SES scores. Results from this multicountry study demonstrate the validity of a simplified SES index. With further validation, this simplified index may provide a standard approach for SES adjustment across resource-limited settings.

Impact of socioeconomic adjustment on physicians' relative cost of care.

Ongoing efforts to profile physicians on their relative cost of care have been criticized because they do not account for differences in patients' socioeconomic status (SES). The importance of SES adjustment has not been explored in cost-profiling applications that measure costs using an episode of care framework. We assessed the relationship between SES and episode costs and the impact of adjusting for SES on physicians' relative cost rankings. We analyzed claims submitted to 3 Massachusetts commercial health plans during calendar years 2004 and 2005. We grouped patients' care into episodes, attributed episodes to individual physicians, and standardized costs for price differences across plans. We accounted for differences in physicians' case mix using indicators for episode type and a patient's severity of illness. A patient's SES was measured using an index of 6 indicators based on the zip code in which the patient lived. We estimated each physician's case mix-adjusted average episode cost and percentile rankings with and without adjustment for SES. Patients in the lowest SES quintile had $80 higher unadjusted episode costs, on average, than patients in the highest quintile. Nearly 70% of the variation in a physician's average episode cost was explained by case mix of their patients, whereas the contribution of SES was negligible. After adjustment for SES, only 1.1% of physicians changed relative cost rankings >2 percentiles. Accounting for patients' SES has little impact on physicians' relative cost rankings within an episode cost framework.

The Impact of Adjustment for Socioeconomic Status on Comparisons of Cancer Incidence between Two European Countries

Background. Cancer incidence rates vary considerably between countries and by socioeconomic status (SES). We investigate the impact of SES upon the relative cancer risk in two neighbouring countries. Methods. Data on 229,824 cases for 16 cancers diagnosed in 1995–2007 were extracted from the cancer registries in Northern Ireland (NI) and Republic of Ireland (RoI). Cancers in the two countries were compared using incidence rate ratios (IRRs) adjusted for age and age plus area-based SES. Results. Adjusting for SES in addition to age had a considerable impact on NI/RoI comparisons for cancers strongly related to SES. Before SES adjustment, lung cancer incidence rates were 11% higher for males and 7% higher for females in NI, while after adjustment, the IRR was not statistically significant. Cervical cancer rates were lower in NI than in RoI after adjustment for age (IRR: 0.90 (0.84–0.97)), with this difference increasing after adjustment for SES (IRR: 0.85 (0.79–0.92)). For cancers with a weak or nonexistent relationship to SES, adjustment for SES made little difference to the IRR. Conclusion. Socioeconomic factors explain some international variations but also obscure other crucial differences; thus, adjustment for these factors should not become part of international comparisons.

The burden of disease associated with being African-American in the United States and the contribution of socio-economic status

The burden of disease associated with being African-American in the US, and the contribution of socio-economic status (SES) to that burden have not been quantified. We derived burden of disease estimates for African-Americans and whites by age group, with and without adjustment for SES (income and education). We used (1) EQ-5D Index scores from the 2000 US Medical Expenditure Panel Survey to derive quality-adjusted life year (QALY) compatible estimates of health-related quality of life (HRQL); (2) 1990–1992 US National Health Interview Survey data linked to National Death Index data through 1995 to derive mortality risks; and (3) 2000 US mortality data from the National Center for Health Statistics to derive current mortality estimates for the US population. We found that relative to whites, African-Americans suffer 67,000 more deaths annually, resulting in 2.2 million years of life lost, and 1.1 million years after SES adjustment. Total QALYs lost (HRQL and mortality) dropped from 2.3 million to 902,000 after SES adjustment. SES differences between African-Americans and whites appear to explain all the HRQL disparity but only half the mortality disparity. Better understanding of the disparate effects of SES may inform interventions to address health disparities adversely affecting African-Americans.

Prostate cancer screening between low-income African-American and Caucasian men ☆

Objective African-Americans (AA) are more likely than Caucasians (CA) to be diagnosed with advanced prostate cancer, perhaps due to delayed detection. We investigated racial differences in prostate cancer screening according to age and socioeconomic and demographic indices in a large and predominantly low-income population. Methods In-person interviews were conducted with 12,552 men, 84% AA, recruited during 2002 through 2004 from 25 community health centers in the southern United States. Prostate specific antigen test (PSA) and digital rectal examination (DRE) histories, and socioeconomic and demographic indices (i.e., education, household income, health insurance, and marital status) were determined. Odds ratios (OR) from logistic regression summarized the screening and race association as a function of age, while controlling for socioeconomic status (SES). Results Racial differences in screening prevalence varied with age. Of men older than 65 years, CA were significantly more likely to report a PSA test (OR = 1.4) or DRE (OR = 1.5) within the past 12 months. However, these disparities were reduced with control for SES (PSA: OR =1.2; DRE: OR = 1.3, P > 0.05). In contrast, at ages younger than 65, CA were equally or less likely to have received a recent PSA test or DRE, particularly at ages 45–49 years (PSA: OR = 0.7; DRE: OR = 0.9), with little change after SES adjustment. Conclusions Consistent with several screening recommendations, younger AA men, especially those younger than age 50, are more likely than CA to have had a recent PSA test or DRE, independent of SES. Of men older than age 65, less frequent use of screening among AA than CA seems partly attributable to SES and factors other than race.

Occupational physical activity, socioeconomic status, and risks of 15 cancer sites in Turkey.

A multiple-site case-control study of 15 cancers (stomach; colon; rectum; larynx; lung; melanoma; skin; female breast; male breast; cervix; ovary; uterus; prostate; testis; and bladder) was conducted to evaluate their association with occupational physical activity and socioeconomic status (SES). A hospital-based study population (3,486 male cases and 379 female cases, and 2,127 male and 244 female controls) was established in an oncological treatment center in Istanbul, Turkey, from 1979-84. Assessment of physical activity and SES was based on job titles held by the study subjects. Two measures of physical activity were developed based on energy expenditure and 'sitting time' during working hours. Observed risks were adjusted for age, smoking, and SES. Elevated risks were observed among workers who held sedentary jobs for cancers of the colon (odds ratio [OR] = 1.6), rectum (OR = 1.3), melanoma (OR = 1.9), male breast (OR = 1.4), prostate (OR = 5.0), and ovary (OR = 2.0). Cancers of the cervix and uterus showed significantly decreasing risks with decreased activity. Risks of cancers of the colon, rectum, larynx, ovary, and melanoma were enhanced after risks for physical activity indices were adjusted for SES, while the associations between physical activity and cancers of the prostate, cervix, and uterus were weakened after SES adjustment. Risks of melanoma rose significantly with both activity indices after SES adjustment.(ABSTRACT TRUNCATED AT 250 WORDS)