Individual Socioeconomic Status Measures Research Articles

Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis.

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor. This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status. Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs). Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79). Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Abstract C012: Integration of social variables resolves subclasses of tumor microenvironment

Abstract Intro: Recent work has identified subclasses of the tumor microenvironment, with most work emphasizing a robust adaptive immune response associated with ER-negative and/or basal-like tumors. Since adaptive immune responses may influence response to chemotherapy and may be targetable by immunotherapy, understanding population-level heterogeneity in immune response is important. Methods: We leveraged the Carolina Breast Cancer Study (CBCS), a large population-based study that oversampled young (≤50 years) and Black women with invasive breast cancer (BC). We performed NanoString gene expression profiling on tissue from 1,329 BC patients, including 704 (53%) Black and 625 (47%) non-Black women, and assayed genes representative of at least 10 individual immune cell types from both adaptive and innate arms of the immune system, homologous recombination deficiency signature and TP53 mutation status. Using a latent class approach that included variables for self-reported race, triple negative status, individual measures of socioeconomic status and community-level measures of advantage and disadvantage, we identified latent classes that integrated tumor microenvironment with tumor biology, individual and community level exposures under a Cells-to-Society conceptual model of breast cancer outcomes. Results: Latent Class Analysis identified 6 integrated classes of the tumor microenvironment, further subdividing a previously identified and validated adaptive immune class into two subgroups. The two adaptive classes were distinguished by different expression levels of B-cells, CD8+ T cells, cytotoxic cells and PD-L1, and differing frequency of basal-like subtype (Adaptive Class 1 RFD [95% CI]: 19.0 [11.9, 26.2]; Adaptive Class 2 RFD [95% CI]: 32.8 [23.3,42.3]) and presence of genomic instability gene expression subtypes (Adaptive Class-1 RFD [95% CI]: 22.1 [14.0, 30.1]; Adaptive Class-2 RFD [95% CI]: 37.5 [28.1,46.2]) relative to the immune-quiet tumors. Discussion: Social variables have complex effects on tumor biology and microenvironment, but latent class models and other model-based approaches may help to integrate biological data with other social factors to identify susceptible subgroups and to optimize precision medicine in diverse populations. Citation Format: Alina M. Hamilton, Kara McCormack, Marc A. Emerson, Melissa A. Troester, Terry Hyslop. Integration of social variables resolves subclasses of tumor microenvironment [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C012.

Abstract A103: Joint associations of education and neighborhood socioeconomic status on smoking prevalence: The Multiethnic Cohort Study

Abstract Background: Smoking is the leading cause of cancer and cancer death. Individual- and neighborhood-level measures of socioeconomic status (SES) have been separately associated with racial and ethnic differences in smoking rates. However, the joint associations of individual- and neighborhood-level SES on smoking have not been studied. Thus, we examined the joint association of education and neighborhood SES (nSES) on smoking status in the Multiethnic Cohort (MEC) study. Methods: We conducted a cross-sectional study of the joint association of nSES and education with smoking for 166,475 MEC participants (26,350 African American, 50,207 Japanese American, 34,490 Latino, 12,326 Native Hawaiian, and 43,102 White participants) recruited from predominately Los Angeles County and Hawaii. Smoking status, education, and other demographics and health behaviors were reported at baseline (1993-1996). Neighborhood SES (nSES) was derived using 1990 US Census data on education, occupation, unemployment, household income, poverty, rent, and house values and assigned to geocoded baseline addresses; and categorized into quintiles using LA County and Hawaii nSES distributions. The joint nSES and education measure was categorized into 4 levels using high nSES (quintiles 4-5), low nSES (quintiles 1-3), high education (≥ high school), and low education (< high school). The study outcome was current smoking vs. non-smoking (never or former smoking). Poisson regression was used to estimate prevalence ratios (PR) and 95% CIs for the joint association of nSES and education with current smoking, adjusting for sex, race/ethnicity, marital status, physical activity, occupation, alcohol, obesity, 2015 healthy eating index, and clustering by census block group. Subgroup analyses were conducted by state (CA and HI), sex, and race/ethnicity. Results: For CA MEC participants, smoking prevalence was highest with low nSES and low education, followed by low nSES and high education, and high nSES and low education (PR=1.50, 1.33, and 1.29, respectively). This pattern was found in females but not males and in African American and Japanese American participants but not Latino or White participants. For HI MEC participants, smoking prevalence was highest with low nSES and low education, followed by high nSES and low education, and low nSES and high education (PR=1.41, 1.36, and 1.20, respectively). This pattern was found in males and females in HI and across all racial and ethnic groups. Interestingly, in Native Hawaiian participants, the smoking prevalence with low nSES and low education was similar to those with high nSES and low education (PR=1.54 and 1.52, respectively). Conclusion: In the MEC, smoking prevalence was highest in CA for females, African American, and Japanese American participants in joint SES categories with low nSES, while in HI, low education appeared more influential, especially among Native Hawaiian participants. These findings demonstrate the importance of considering both individual and neighborhood-level SES measures on smoking prevalence. Citation Format: Catherine P. Walsh, Younghan Lee, Salma Shariff-Marco, Lynne Wilkens, Loic Le Marchand, Christopher A. Haiman, Iona Cheng, S. Lani Park. Joint associations of education and neighborhood socioeconomic status on smoking prevalence: The Multiethnic Cohort Study [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr A103.

Neighborhood Deprivation and Neural Tube Defects.

Individual measures of socioeconomic status (SES) have been associated with an increased risk of neural tube defects (NTDs); however, the association between neighborhood SES and NTD risk is unknown. Using data from the National Birth Defects Prevention Study (NBDPS) from 1997 to 2011, we investigated the association between measures of census tract SES and NTD risk. The study population included 10,028 controls and 1829 NTD cases. We linked maternal addresses to census tract SES measures and used these measures to calculate the neighborhood deprivation index. We used generalized estimating equations to calculate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) estimating the impact of quartiles of census tract deprivation on NTDs adjusting for maternal race-ethnicity, maternal education, and maternal age at delivery. Quartiles of higher neighborhood deprivation were associated with NTDs when compared with the least deprived quartile (Q2: aOR = 1.2; 95% CI = 1.0, 1.4; Q3: aOR = 1.3, 95% CI = 1.1, 1.5; Q4 (highest): aOR = 1.2; 95% CI = 1.0, 1.4). Results for spina bifida were similar; however, estimates for anencephaly and encephalocele were attenuated. Associations differed by maternal race-ethnicity. Our findings suggest that residing in a census tract with more socioeconomic deprivation is associated with an increased risk for NTDs, specifically spina bifida.

Social determinants of cognitive outcomes in survivors of pediatric brain tumors treated with conformal radiation therapy.

Social determinants of health including parental occupation, household income, and neighborhood environment are predictors of cognitive outcomes among healthy and ill children; however, few pediatric oncology studies have investigated this relationship. This study utilized the Economic Hardship Index (EHI) to measure neighborhood-level social and economic conditions to predict cognitive outcomes among children treated for brain tumors (BT) with conformal radiation therapy (RT). Two hundred and forty-one children treated on a prospective, longitudinal, phase II trial of conformal photon RT (54-59.4 Gy) for ependymoma, low-grade glioma, or craniopharyngioma (52% female, 79% white, age at RT = 7.76 ± 4.98 years) completed serial cognitive assessments (intelligence quotient [IQ], reading, math, and adaptive functioning) for ten years. Six US census tract-level EHI scores were calculated for an overall EHI score: unemployment, dependency, education, income, crowded housing, and poverty. Established socioeconomic status (SES) measures from the extant literature were also derived. Correlations and non-parametric tests revealed EHI variables share modest variance with other SES measures. Income, unemployment, and poverty overlapped most with individual SES measures. Linear mixed models, accounting for sex, age at RT, and tumor location, revealed EHI variables predicted all cognitive variables at baseline and change in IQ and math over time, with EHI overall and poverty most consistent predictors. Higher economic hardship was associated with lower cognitive scores. Neighborhood-level measures of socioeconomic conditions can help inform understanding of long-term cognitive and academic outcomes in survivors of pediatric BT. Future investigation of poverty's driving forces and the impact of economic hardship on children with other catastrophic diseases is needed.

Association of Early and Late Hospital Readmissions with a Novel Housing-Based Socioeconomic Measure.

BackgroundWhile socioeconomic status has been linked to hospital readmissions for several conditions, reliable measures of individual socioeconomic status are often not available. HOUSES, a new measure of individual socioeconomic status based upon objective public data about one's housing unit, is inversely associated with overall hospitalization rate but it has not been studied with respect to readmissions.PurposeTo determine if patients in the lowest HOUSES quartile are more likely to be readmitted within 30 days (short-term) and 180 days (long-term).MethodsA retrospective cohort study of 11 993 patients having 21 633 admissions was conducted using generalized linear mixed-effects models.ResultsHOUSES quartile did not show any significant association with early readmission. However, when compared to the lowest HOUSES quartile, the second quartile (OR = 0.90, 95%CI 0.83-0.98) and the third quartile (OR = 0.91, 95%CI 0.83-0.99) were associated with lower odds of late readmission while the highest quartile (OR = 0.91, 95%CI 0.82-1.01) was not statistically different.ConclusionHOUSES was associated with late readmission, but not early readmission. This may be because early readmissions are influenced by medical conditions and hospital care while late readmissions are influenced by ambulatory care and home-based factors. Since HOUSES relies on public county assessor data, it is generally available and may be used to focus interventions on those at highest risk for late readmission.

Frailty associations with socioeconomic status, healthcare utilisation and quality of life among older women residing in regional Australia.

Objectives:The health and well-being of older women may be influenced by frailty and low socioeconomic status (SES). This study examined the association between frailty and SES, healthcare utilisation and quality of life (QOL) among older women in regional Australia.Methods:Cross-sectional analysis of the Geelong Osteoporosis Study was conducted on 360 women (ages ≥60yr) in the 15-year follow up. Frailty was identified using modified Fried’s phenotype. Individual SES measures and healthcare utilisation were documented by questionnaire. Area-based SES was determined by cross-referencing residential addresses with the Australian Bureau of Statistics Index of Relative Socio-economic Advantage and Disadvantage (IRSAD). QOL was measured using the Australian World Health Organisation Quality of Life Instrument (WHOQoL-Bref). Multinomial logistic regression was conducted with frailty groupings as outcome.Results:Sixty-two (17.2%) participants were frail, 199 (55.3%) pre-frail and 99 (27.5%) robust. Frail participants were older with higher body mass index. Frailty was associated with lower education but not marital status, occupation or IRSAD. Strong associations with frailty were demonstrated for all WHOQoL-Bref domains. Frailty was associated with more primary care doctor visits (p<0.001).Conclusions:This population-based study highlights the significant impact of frailty on older women, indicating reduced QOL and increased primary care doctor visits.

Is place or person more important in determining higher rural cancer mortality? A data-linkage study to compare individual versus area-based measures of deprivation.

Data from Northeast Scotland for 11,803 cancer patients (diagnosed 2007-13) were linked to UK Censuses to explore relationships between hospital travel-time, timely-treatment and one-year-mortality, adjusting for both area and individual-level socioeconomic status (SES). Adjusting for area-based SES, those living >60 minutes from hospital received timely-treatment more often than those living <15 minutes. Substituting individual-level SES changed little. Adjusting for area-based SES those living >60 minutes from hospital died within one year more often than those living <15 minutes. Again, substituting individual-level SES changed little. In Northeast Scotland distance to services, rather than individual SES, likely explains poorer rural cancer survival.Background and objectiveThe Northeast and Aberdeen Scottish Cancer and Residence (NASCAR) study found rural-dwellers are treated quicker but more likely to die within a year of a cancer diagnosis. A potential confounder of the relationship between geography and cancer mortality is socioeconomic status (SES). We linked the original NASCAR cohort to the UK Censuses of 2001 and 2011, at an individual level, to explore the relationship between travel time to key healthcare facilities, timely cancer treatment and one-year mortality adjusting for both area and individual-level markers of socioeconomic status.MethodsA data linkage study of 11803 patients examined the association between travel times, timely treatment and one-year mortality with adjustment for area, and for individual-level, markers of socioeconomic status.ResultsFollowing adjustment for area-based SES measures those living more than 60 minutes from the cancer treatment centre were significantly more likely to be treated within 62 days of GP referral than those living within 15 minutes (Odds Ratio [OR]) 1.41; 95% (Confidence Interval [CI]) 1.23, 1.60]. Replacing area-based with individual-level SES measures from UK Censuses made little impact on the results [OR 1.39; 95% CI 1.22, 1.57].Following adjustment for area-based SES measures of socioeconomic status those living more than 60 minutes from the cancer treatment centre were significantly more likely to die within one year than those living closer by [OR 1.22; 95% CI 1.08, 1.38]. Again, replacing area-based with individual-level SES measures from UK Censuses made little impact on the result [OR 1.20; CI 1.06, 1.35].ConclusionsDistribution of individual measures of socioeconomic status did not differ significantly between rural and urban cancer patients. The relationship between distance to service, timely treatment and one-year survival were the same adjusting for both area-based and individual SES. Overall, it seems that distance to services, rather than personal characteristics, influences poorer rural cancer survival.

Ethnicity, Neighborhood and Individual Socioeconomic Status, and Obesity: The Singapore Multiethnic Cohort.

It remains unclear whether ethnicity has an impact on obesity independent of socioeconomic status (SES) and environmental factors. Singapore provides a unique opportunity to address this issue because three major Asian ethnic groups are represented, and government policies prevent ethnic segregation. Therefore this study examined associations between ethnicity, SES, and obesity within neighborhoods in Singapore. Cross-sectional data from 29,819 participants of the Singapore Multiethnic Cohort who were aged 21 to 75 years and of Chinese, Malay, and Indian ethnicity were used. Obesity was defined using Asian criteria. Multilevel models used obesity as the dependent variable and age, marital status, ethnicity, education level, income, and neighborhood SES as independent variables. Education level was more strongly inversely associated with obesity than income level or neighborhood SES. The association between ethnicity and obesity was not substantially explained by measures of individual and neighborhood SES. In females, the fully adjusted odds ratio of obesity was 5.01 for Malay ethnicity and 4.81 for Indian ethnicity as compared with Chinese ethnicity. In males, these odds ratios were 2.61 and 2.07, respectively. Ethnicity was strongly associated with obesity independent of SES and neighborhood environment. More research on sociocultural factors contributing to ethnic differences in obesity is warranted.

Socioeconomic inequalities in cancer incidence in Europe: a comprehensive review of population-based epidemiological studies

BackgroundSince the end of the previous century, there has not been a comprehensive review of European studies on socioeconomic inequality in cancer incidence. In view of recent advances in data source linkage and analytical methods, we aimed to update the knowledge base on associations between location-specific cancer incidence and individual or area-level measures of socio-economic status (SES) among European adults.Materials and methodsWe systematically searched three databases (PubMed, Scopus and Web of Science) for articles on cancer incidence and SES. Qualitative synthesis was performed on the 91 included English language studies, published between 2000 and 2019 in Europe, which focused on adults, relied on cancer registry data and reported on relative risk (RR) estimates.ResultsAdults with low SES have increased risk of head and neck, oesophagogastric, liver and gallbladder, pancreatic, lung, kidney, bladder, penile and cervical cancers (highest RRs for lung, head and neck, stomach and cervix). Conversely, high SES is linked with increased risk of thyroid, breast, prostate and skin cancers. Central nervous system and haematological cancers are not associated with SES. The positive gap in testicular cancer has narrowed, while colorectal cancer shows a varying pattern in different countries. Negative associations are generally stronger for men compared to women.ConclusionsIn Europe, cancers in almost all common locations are associated with SES and the inequalities can be explained to a varying degree by known life-style related factors, most notably smoking. Independent effects of many individual and area SES measures which capture different aspects of SES can also be observed.

Neighborhood socioeconomic disadvantage is associated with multimorbidity in a geographically-defined community

BackgroundPersons with low socioeconomic status may be disproportionately at risk for multimorbidity.MethodsAdults aged ≥20 years on 4/1/2015 from 7 counties in Minnesota were identified using the Rochester Epidemiology Project (population-based sample). A composite measure of neighborhood socioeconomic disadvantage, the area deprivation index (ADI), was estimated at the census block group level (n = 251). The prevalence of 21 chronic conditions was obtained to calculate the proportion of persons with multimorbidity (≥2 chronic conditions) and severe multimorbidity (≥5 chronic conditions). Hierarchical logistic regression was used to estimate the association of ADI with multimorbidity and severe multimorbidity using odds ratios (OR).ResultsAmong 198,941 persons (46.7% male, 30.6% aged ≥60 years), the age- and sex-standardized (to the United States 2010 census) median prevalence (Q1, Q3) was 23.4% (21.3%, 25.9%) for multimorbidity and 4.8% (4.0%, 5.7%) for severe multimorbidity. Compared with persons in the lowest quintile of ADI, persons in the highest quintile had a 50% increased risk of multimorbidity (OR 1.50, 95% CI 1.39–1.62) and a 67% increased risk of severe multimorbidity (OR 1.67, 95% CI 1.51–1.86) after adjusting for age, sex, race, and ethnicity. Associations were stronger after further adjustment for individual level of education; persons in the highest quintile had a 78% increased risk of multimorbidity (OR 1.78, 95% CI 1.62–1.96) and a 92% increased risk of severe multimorbidity (OR 1.92, 95% CI 1.72–2.13). There was evidence of interactions between ADI and age, between ADI and sex, and between ADI and education. After age 70 years, no difference in the risk of multimorbidity was observed across quintiles of ADI. The pattern of increasing multimorbidity with increasing ADI was more pronounced in women. Finally, there was less variability across quintiles of ADI for the most highly educated group.ConclusionsHigher ADI was associated with increased risk of multimorbidity, and the associations were strengthened after adjustment for individual level of education, suggesting that neighborhood context plays a role in health above and beyond individual measures of socioeconomic status. Furthermore, associations were more pronounced in younger persons and women, highlighting the importance of interventions to prevent chronic conditions in younger women, in particular.

Factors associated with health facility delivery in West and Central Africa: A multilevel analysis of seven countries

We examined associations between individual and community socioeconomic status (SES) and childbirth in a health facility in West and Central Africa using data from the 2009–2011 United Nations Children's Fund (UNICEF) Multiple Indicator Cluster Survey for women in seven countries (n = 34,487). Individual SES measures were education and wealth; community SES was low or high poverty index. In adjusted results, women residing in communities with high poverty had significantly lower odds of facility delivery than those who lived in more affluent communities in five countries (all p < 0.001). Reducing out-of-pocket healthcare costs may improve women’s access to delivery in health facilities.

Global Patterns of Environmental Inequities within the Prospective Urban and Rural Epidemiology Study

OPS 04: Environmental justice, Room 117, Floor 1, August 27, 2019, 1:30 PM - 3:00 PM Background: Most environmental justice research has been conducted in developed countries, but the largest environmental inequities may exist at a global level and within developing countries. Here, we quantify the associations between diverse environmental exposures and socioeconomic status (SES) within a global cohort study. Methods: We leverage the 167,782 participants from 21 countries of the Prospective Urban and Rural Epidemiology cohort, spanning low to high socioeconomic development. We examined outdoor PM2.5 and NO2 air pollution, household air pollution (HAP), greenness, park access, household water access, perceived local traffic and local pollution, extreme heat days, and change in extreme heat days over a 15-year period. We compared these exposures with individual measures of SES, including education, occupation, a household wealth index, and percent income spent on food. We summarized exposure levels across SES strata and applied multinomial logistic regression models to determine associations between environmental exposures with SES, controlling for country income level, population density and urban/rural status. We examined global and country-specific environmental inequalities stratified by urban and rural communities. Results: Across all communities, we observed the largest SES disparities for HAP, extreme temperature, increase in extreme temperature days, and decreased green space. For example, 41% of individuals with less than a primary school education cooked with solid fuels compared to 6% of individuals with post-secondary education. Individuals with less than primary school education experienced 27 extreme heat days at baseline year compared to 20 for individuals with post-secondary education. Several environmental exposures were also increased with higher SES, including NO2 and perceptions of local traffic and pollution. Substantial differences were observed between urban/rural communities and in country-specific analyses. Conclusion: While environmental risks are associated with SES, the relationships are complex and vary by exposure and local context. It is essential for research and policy to recognize these multifaceted relationships.

Socio-economic inequalities in fragility fracture outcomes: a systematic review and meta-analysis of prognostic observational studies.

Fragility fractures, especially of the hip, cause substantial excess mortality and impairment in health-related quality of life (HRQoL). This systematic review and meta-analysis aimed to investigate the association between socio-economic status (SES) and post-fracture mortality and HRQoL. PubMed, EMBASE and CINAHL databases were searched from inception to the last week of November 2018 for studies reporting an association between SES and post-fracture mortality and/or HRQoL among people aged ≥ 50years. Risk ratios (RRs) were meta-analyzed using a standard inverse-variance-weighted random effects model. Studies using individual-level and area-based SES measures were analyzed separately. A total of 24 studies from 15 different countries and involving more than one million patients with hip fractures were included. The overall risk of mortality within 1-year post-hip fracture in individuals with low SES was 24% higher than in individuals with high SES (RR 1.24, 95% CI 1.19 to 1.29) for individual-level SES measures, and 14% (RR 1.14, 95% CI 1.09 to 1.19) for area-based SES measures. The quality of the evidence for the outcome mortality was moderate. Using individual SES measures, we estimated the excess HRQoL loss to be 5% (95% CI - 1 to 10%) among hip fracture patients with low SES compared with high SES. We found a consistently increased risk of post-hip fracture mortality with low SES across SES measures and across countries with different political structures and different health and social care infrastructures. The impact of SES on post-fracture HRQoL remains uncertain due to sparse and low-quality evidence.

Relationship of Socioeconomic Status Indicators to Obesity in Hispanic and Non-Hispanic White Middle-Aged Women: A Population Study.

Obesity rates differ between Hispanic and White (non-Hispanic) women in the United States, with higher rates among Hispanic women. Socioeconomic processes contribute to this disparity both at the individual and the environmental level. Understanding these complex relationships requires multilevel analyses within cohorts of women that have a shared environment. In population-based samples of Hispanic and White (non-Hispanic) women from the same neighborhoods, we evaluated within each ethnic group a) The association of individual-level socioeconomic status (SES) with body mass index (BMI); and b) The additional contribution of neighborhood-level measures of SES. Using population-based multi-stage sampling methods, we oversampled low SES and Hispanic block groups. During household screening, we identified women aged 30 to 50 years. Among White women, we specifically oversampled women with low educational levels. 515 Hispanic and 503 White women completed baseline. Height and weight were measured. Baseline surveys, in Spanish and English, included four measures of SES. Three measures of area-level SES were examined. Analysis of loge BMI on each SES measure used linear mixed models, incorporating design effects. Among White women, low education, social status, and neighborhood SES were associated with higher BMI (p < 0.001, p < 0.0001, and p < 0.05, respectively), independent of other SES measures. Although the highest grouped category of education, income and subjective social status within the Hispanic cohort had the lowest mean estimated BMI, the point estimates across categories were not monotonic, and had wide confidence intervals. As a result, in contrast to the findings among White women, no statistically significant associations were found between BMI and measures of SES among Hispanic women. Neighborhood and individual measures of SES operate differently in Hispanic compared with White women. We had assumed the measures we included to be most salient and operate similarly for both groups of women. Rather the salient factors for Hispanic women have yet to be identified. Improved understanding may ultimately inform the design of culturally-relevant multilevel obesity prevention strategies.

Various socioeconomic factors as predictors of internalizing and externalizing disorders in children and adolescents

Various socioeconomic factors as predictors of internalizing and externalizing disorders in children and adolescents Abstract. Objective: Socioeconomic status (SES) is an important risk factor for psychiatric disorders in children and adolescents. Various SES parameters are included in research efforts. This paper investigates the predictive value of different individual SES measures and the combination thereof regarding internalizing and externalizing disorders in children and adolescents. Method: We used data from N = 381 8- to 14-year-olds who had been recruited in child and adolescent psychiatric facilities and in the general population. Based on a diagnostic parent interview (K-SADS-PL), we divided the sample into internalizing and externalizing groups and a healthy control group. The SES data of both parents were collected. Education, occupational status, and income were integrated as single factors as well as combined factor to form the SES index. Additionally, we assessed unemployment. Results: Using multivariate analyses, we controlled for age and sex. All single factors turned out to be significant risk factors for internalizing and externalizing disorders. Occupational status was the most important single factor. The explained variance for the SES index was as high as for occupational status. Unemployment was not predictive when examined simultaneously with the SES index. Conclusion: Future studies should integrate the SES as risk factor and use the SES index or occupational status as single factor. The integration of unemployment is not necessarily needed.

Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country

BackgroundExplorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka.MethodsA sample of 1300 participants, (aged 35–64 years) randomly selected using a stratified multi-stage cluster sampling method, were studied employing a cross sectional descriptive design. The socioeconomic indicators (SEIs) of the individual were education level and occupational category, and at the household level, the household income, social status level and area deprivation level. DM was diagnosed if the fasting plasma glucose was ≥126 and a body mass index (BMI) of > 27.5 kg/m2 was considered high. Asian cut-off values were used for high waist circumference (WC). Validated tools were used to assess the diet and level of physical activity. The slope index of inequality (SII), relative index of inequality (RII) and concentration index (CI) were used to assess inequalities.ResultsThe prevalence of DM and its risk factors (at individual or household level) showed no consistent relationship with the three measures of inequality (SII, RII and CI) of the different indices of socio economic status (education, occupation, household income, social status index or area unsatisfactory basic needs index).The prevalence of diabetes showed a more consistent pro-rich distribution in females compared to males. Of the risk factors in males and females, the most consistent and significant pro-rich relationship was for high BMI and WC. In males, the significant positive relationship with high BMI for SII ranged from 0.18 to 0.35, and RII from 1.56 to 2.25. For high WC, the values were: SII from 0.13 to 0.27 and RII from 1.9 to 3.97. In females the significant positive relationship with high BMI in SII ranged from 0.13 to 0.29, and RII from 2.3 to 4.98. For high WC the values were: SII from 028 to 0.4 and RII 1.99 to 2.39.Of the other risk factors, inadequate fruit intake showed a consistent significant pro-poor distribution only in males using SII (− 0.25 to − 0.36) and in both sexes using CI. Smoking also showed a pro-poor distribution in males especially using individual measures of socio-economic status (i.e. education and occupation).ConclusionsThe results show a variable relationship between socioeconomic status and prevalence of diabetes and its risk factors. The inequalities in the prevalence of diabetes and risk factors vary depending on gender and the measures used. The study suggests that measures to prevent diabetes should focus on targeting specific factors based on sex and socioeconomic status. The priority target areas for interventions should include prevention of obesity (BMI and central obesity) specifically in more affluent females. Males who have a low level of education and in non-skilled occupations should be especially targeted to reduce smoking and increase fruit intake.

A systematic review of socioeconomic status measurement in 13 years of U.S. injury research

ObjectiveThe purpose of this review was to assess the impact of socioeconomic status (SES) on injury and to evaluate how U.S. injury researchers have measured SES over the past 13years in observational research studies. Design & methodsThis systematic review included 119 US injury studies indexed in PubMed between January 1, 2002 and August 31, 2015 that used one or more individual and/or area-level measures of SES as independent variables. Study findings were compared to the results of a previous review published in 2002. ResultsFindings indicate SES remains an important predictor of injury. SES was inversely related to injury in 78 (66%) of the studies; inverse relationships were more consistently found in studies of fatal injury (77.4%) than in studies of non-fatal injury (58%). Approximately two-thirds of the studies (n=73, 61%) measured SES along a gradient and 59% used more than one measure of SES (n=70). Studies that used a gradient measure of SES and/or more than one measure of SES identified significant relationships more often. These findings were essentially equivalent to those of a similar 2002 review (Cubbin & Smith, 2002). ConclusionsThere remains a need to improve measurement of SES in injury research. Public health training programs should include best practices for measurement of SES, which include: measuring SES along a gradient, selecting SES indicators based on the injury mechanism, using the smallest geographic region possible for area-level measures, using multiple indicators when possible, and using both individual and area-level measures as both contribute independently to injury risk. Area-level indicators of SES are not accurate estimates of individual-level SES. Practical applicationsInjury researchers should measure SES along a gradient and incorporate individual and area-level SES measures that are appropriate to the injury outcome under study.

Screen-time, Physical Activity And Bmi

PURPOSE: Observational studies often adjust for confounding by socioeconomic status (SES) but rarely report the magnitude of confounding bias. Also, different indicators of SES may not meet the definition of a confounder for some associations. This study will determine whether different measures of SES have different confounding effects on the associations of screen time with BMI and of moderate/vigorous physical activity (MVPA) with BMI. METHODS: In a 15 year prospective cohort study, 1830 young adults (age 25 - 36) reported height and weight (used to calculate BMI in kg/m2), hours/day of screen time, hours/week of MVPA and nine measures of SES in 2015. SES at baseline (2000) was based on parent education and employment. BMI, screen time and MVPA were regressed with linear regression on each SES measure and a composite measure that combined young adults’ income and education. Crude associations of screen time with BMI and of MVPA with BMI were assessed with linear regression. The SES measures were added individually to these models to determine the percent change in the associations (percent confounding). RESULTS: Seven measures of SES were associated with BMI; eight with screen time; and four with MVPA. Education, income, financial strain and living with parents all confounded the association of screen time with BMI by more than 10%. No measure confounded the relationship of MVPA with BMI by more than 10%. The composite measure was significantly associated with screen time (β = -0.39, 95% CI [-0.47 to -0.31]) and BMI (β = -1.29, 95% CI [-1.58 to -0.99]), but not with MVPA. This composite substantially confounded the association of screen time with BMI (29%) but not of MVPA with BMI (3%). CONCLUSIONS: The composite SES measure had similar properties to individual SES measures: it was associated with screen time and BMI but not with MVPA and it had substantial confounding bias effect on the association of screen time with BMI, but not on the association of MVPA with BMI. Knowing the magnitude of confounding can help assess results from other studies that do not adjust for SES. A composite measure may be useful for the varied analyses done with data from large cohort studies. However, because of the potential for over fitting models, associations with measures of SES should be carefully considered before using them to adjust for confounding.

Local environmental quality positively predicts breastfeeding in the UK's Millennium Cohort Study.

Background and Objectives: Breastfeeding is an important form of parental investment with clear health benefits. Despite this, rates remain low in the UK; understanding variation can therefore help improve interventions. Life history theory suggests that environmental quality may pattern maternal investment, including breastfeeding. We analyse a nationally representative dataset to test two predictions: (i) higher local environmental quality predicts higher likelihood of breastfeeding initiation and longer duration; (ii) higher socioeconomic status (SES) provides a buffer against the adverse influences of low local environmental quality. Methodology: We ran factor analysis on a wide range of local-level environmental variables. Two summary measures of local environmental quality were generated by this analysis-one 'objective' (based on an independent assessor's neighbourhood scores) and one 'subjective' (based on respondent's scores). We used mixed-effects regression techniques to test our hypotheses. Results: Higher objective, but not subjective, local environmental quality predicts higher likelihood of starting and maintaining breastfeeding over and above individual SES and area-level measures of environmental quality. Higher individual SES is protective, with women from high-income households having relatively high breastfeeding initiation rates and those with high status jobs being more likely to maintain breastfeeding, even in poor environmental conditions. Conclusions and Implications: Environmental quality is often vaguely measured; here we present a thorough investigation of environmental quality at the local level, controlling for individual- and area-level measures. Our findings support a shift in focus away from individual factors and towards altering the landscape of women's decision making contexts when considering behaviours relevant to public health.