Background: Socioeconomic status (SES) is an important determinant of health and potential modifier of the effects of environmental contaminants. There has been a lack of comprehensive indices for measuring overall SES in Canada. Here, a more comprehensive SES index is developed aiming to support future studies exploring health outcomes related to environmental pollution in Canada. Methods: SES variables (n=22, Census Canada 2006) were selected based on: cultural identities, housing characteristics, variables identified in Canadian environmental injustice studies and a previous deprivation index (Pampalon index). Principal component analysis with a single varimax rotation (factor loadings=¦60¦) was performed on SES variables for 52974 census dissemination areas (DA). The final index was created by averaging the factor scores per DA according to the three components retained. The index was validated by examining its association with preterm birth (gestational age<37 weeks), term low birth weight (LBW, <2500 g), small for gestational age (SGA, <10 percentile of birth weight for gestational age) and PM2.5 (particulate matter=2.5 µm) exposures in Edmonton, Alberta (1999–2008). Results: Index values exhibited a relatively normal distribution (median=0.11, mean=0.0, SD=0.58) across Canada. Values in Alberta tended to be higher than in Newfoundland and Labrador, Northwest Territories and Nunavut (Pearson chi-square p<0.001 across provinces). Lower quintiles of our index and the Pampalon’s index confirmed know associations with a higher prevalence of LBW, SGA, preterm birth and PM2.5 exposure. Results with our index exhibited greater statistical significance and a more consistent gradient of PM2.5 levels and prevalence of pregnancy outcomes. Conclusions: Our index reflects more dimensions of SES than an earlier index and it performed superiorly in capturing gradients in prevalence of pregnancy outcomes. It can be used for future research involving environmental pollution and health in Canada. These metadata can also be found on SAGE's searchable metadata website: http://sagemetadata.policywise.com/nada/index.php/catalog/14

Here are Yost indexes for census tracts and block groups in the United States for various years from 1990-2019. The Yost index is a composite index of socioeconomic status that consists of a percentile score from 1 (highest SES) to 100 (lowest SES). Data for 1990 and 2000 include the 50 US states plus the District of Columbia. For years after 2000, the data additionally include Puerto Rico. To rescale the index to geographic units smaller than the US, the score column may be used, where scores range from about -1.8 for the highest SES to 1.8 for the lowest SES.More about the Yost index can be found here: Yost K, Perkins C, Cohen R, Morris C, Wright W. Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. ​Cancer Causes and Control​ 2001; 12(8): 703–711.

Yu M, Tatalovich Z, Gibson JT, Cronin KA. Using a composite index of socioeconomic status to investigate health disparities while protecting the confidentiality of cancer registry data. ​Cancer Causes and Control​. 2014; 25(1): 81-92.

BackgroundDietary patterns (DP) are associated with health outcomes in younger adults but there is a lack of evidence in the very old (aged 85+) on DP and their association with sociodemographic factors, lifestyle, health and functioning measures. Higher socioeconomic status (SES) has been linked with healthier DP but it is not known whether these associations are sustained in the very old.ObjectiveWe aimed to (a) characterise DP in the very old and (b) assess the relationships between three SES indicators (education, occupational class and area-deprivation index [IMD]) and DP.MethodsComplete dietary data at baseline (2006/07) for 793 participants in the Newcastle 85+ Study were established through 24-hr multiple pass recall. We used Two-Step clustering and 30 food groups to derive DP, and multinomial logistic regression models to assess the association with SES.ResultsWe identified three distinct DP (characterised as ‘High Red Meat’, ‘Low Meat’, and ‘High Butter’) that varied with key sociodemographic, health and functioning measures. ‘Low Meat’ participants were more advantaged (i.e. higher education and occupational class, and lived in more affluent areas in owned homes), were least disabled, cognitively impaired, and depressed, and were more physically active than those in the other DP. After adjusting for other lifestyle factors, cognitive status and BMI, lower educational attainment remained a significant predictor of ‘High Red Meat’ and ‘High Butter’ membership compared with ‘Low Meat’ (‘High Red Meat’: OR [95% CI] for 0–9 and 10–11 years of education vs. ≥12 years: 5.28 [2.85–9.79], p

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. The indicators are the percent of occupied housing units with more than one person per room (i.e., crowded housing); the percent of households living below the federal poverty level; the percent of persons in the labor force over the age of 16 years that are unemployed; the percent of persons over the age of 25 years without a high school diploma; the percent of the population under 18 or over 64 years of age (i.e., dependency); and per capita income. Indicators for Chicago as a whole are provided in the final row of the table.

In the first quarter of 2024, natural person small enterprises in Thailand had the highest potential growth index in the performance dimension, accounting for 0.79 index points. According to the source, the overall potential growth index for these enterprises stood at 0.65 points, which indicated an average growth potential in the country.

Multivariate logistic regressions of CBE practice by SES index (n = 1816).

BackgroundSocioeconomic status (SES) is an important determinant of screen time (ST) in children and adolescents, however, the association between SES and ST is not fully understood in China. This study aimed to investigate the association between SES and ST (operationalized as meeting the ST guidelines; no more than 2 hours per day) in Chinese children and adolescents.MethodsCross-sectional data of 2,955 Chinese children and adolescents aged 8 to 17(53.4% girls) were used. SES was measured using indicators of parental education and perceived family wealth. ST was assessed with detailed items from the Health Behaviour School-aged Children survey questionnaires. Descriptive statistics and a Chi-square test were used to report the sample characteristics and analyse ST differences across different sociodemographic groups. A binary logistic regression was then applied to analyse the association of SES indicators with ST in children and adolescents.ResultsOverall, 25.3% of children and adolescents met the ST guidelines. Children and adolescents with higher parental education levels were 1.84 [95% CI 1.31–2.57; father] and 1.42 [95% CI 1.02–1.98; mother] times more likely to meet the ST guidelines than those with lower parental education levels. Associations between SES and ST varied across sex and grade groups. Moreover, the associations of SES with ST on weekdays and weekends were different.ConclusionsThis study demonstrated the association between SES and ST in children and adolescents, highlighting the importance of targeting children and adolescents with low SES levels as an intervention priority. Based on our findings, specific interventions can be tailored to effectively reduce ST. Future studies are encouraged to use longitudinal or interventional designs to further determine the association between SES and ST.

In the first quarter of 2022, registered juristic small enterprises in Thailand had the highest potential growth index in the operational dimension, accounting for 0.71 index points. According to the source, the overall potential growth index for such enterprises stood at 0.59 points which indicated an average growth potential in the country for firms under this category.

Several individual-based social deprivation and vulnerability indices have been developed to measure the negative impact of low socioeconomic status on health outcomes. However, their variables and measurable characteristics have not been unequivocally assessed. A comprehensive database literature scoping review was performed to identify all individual-based social deprivation and vulnerability indices. Area-based indices and those developed for pediatric populations were excluded. Data were extracted from all eligible studies and their methodology was assessed with quality criteria. A total of 14 indices were identified, of which 64% (9/14) measured social deprivation and 36% (5/14) measured socioeconomic vulnerability. Sum of weights was the most common scoring system, present in 43% (6/14) of all indices, with no exclusive domains to either vulnerability or deprivation indices. A total of 83 different variables were identified; a very frequent variable (29%; 5/14) related to an individual’s social relationships was “seen any family or friends or neighbors.” Only five deprivation indices reported a specific internal consistency measure, while no indices reported data on reproducibility. This is the first scoping review of individual-based deprivation and vulnerability indices, which may be used interchangeably when measuring the impact of SES on health outcomes.

[[NOTE: Placeholder for DOI. Data still in preparation. Data are currently only accessible to qualified reviewers.]] This dataset includes the home mailing addresses of all participants (registrants with at least one courseware action) in MITx and HarvardX courses. For U.S. residents, These mailing addresses can be parsed and geo-matched with data from the US Census to develop a suite of socioeconomic status indicators, including median neighborhood income and neighborhood level of education. We also include self-reported survey data about parental level of education, and we include an indicator for whether or not the participant earned a certificate. These data provide insight into how SES factors predict student enrollment and completion in MOOCs. REQUEST ACCESS by filling out form at http://vpal.harvard.edu/access-vpal-research-data

Socioeconomic status (SES) is a multidimensional construct encompassing objective indicators (e.g., income, education, occupation) and subjective perceptions of one's socioeconomic standing. Both dimensions influence child development and parenting practices, yet studies rarely examine their combined and distinct contributions to children’s daily experiences. This study investigates the unique and overlapping roles of objective and subjective SES in moderating childhood experiences during the preschool years. Participants (N = 162; Mage = 5.77, SD = 0.36; 45.1% girls) were recruited from public schools and a cultural center in Buenos Aires, Argentina. SES was assessed using maternal education (objective SES) and parental perceptions of access to resources (subjective SES). Our findings reveal a partial overlap between the two SES dimensions, with both predicting significant variance in multiple aspects of children’s experiences. Notably, subjective SES uniquely predicted access to specific resources, while objective SES had a stronger influence on most outcomes analyzed. These results underscore the importance of integrating objective and subjective SES measures to capture the nuanced associations between SES and childhood experiences, particularly in diverse socioeconomic contexts. These findings contribute to understanding how SES impacts children’s experiences, offering insights for developing targeted interventions and policies that address diverse socioeconomic realities.

The Census 2021 Relative Socio-Economic Index for SA2 data.Socio-Economic Indexes for Areas (SEIFA) in Australia are comprehensive measures that provide insights into the well-being of communities across the country. Developed by the Australian Bureau of Statistics (ABS), SEIFA indices compile census data to evaluate various socio-economic factors, including income, education, employment, and housing conditions. These indices rank areas in Australia according to their relative socio-economic advantage and disadvantage, offering a detailed snapshot that helps identify the varying levels of social and economic well-being in different regions. This crucial data assists government bodies, policymakers, and community organisations in understanding disparities across different areas, enabling them to tailor services, allocate funding, and develop initiatives that address specific community needs, ultimately aiming to enhance the quality of life and reduce inequalities across different Australian locales.

For further details on how ABS curates these indices please visit: Socio-Economic Indexes for Areas (SEIFA): Technical Paper, 2021 | Australian Bureau of Statistics (abs.gov.au)

Main effects of individual socioeconomic status (SES) and countries’ socioeconomic development (SED) on social relationships: Crude and adjusted results from multi-level models, Odds ratios (OR) or coefficients (Coeff) and 95% confidence intervals (CI).

The Index of Educational Disadvantage for SA Government schools, each year from 2017 (not 2019). The Index of Educational Disadvantage is a socio-economic index, used by the Department for Education to allocate resources to schools to address educational disadvantage related to socio-economic status. The most disadvantaged schools have an index of 1, the least disadvantaged have an index of 7. More information on the Index of Educational Disadvantage is available at https://www.education.sa.gov.au/sites/g/files/net691/f/educational_disadvantage_index_explanation.pdf

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

Abstract: Food insecurity occurs when a household lacks consistent access to food and is more prevalent in ethnic and racial minoritized populations. While there has been a proliferation of research linking food insecurity to obesity, these findings are mixed. It may be helpful to consider some additional geographic factors that may be associated with both factors including socioeconomic status and grocery store density. The purpose of the current study aimed to examine spatial relationships between food insecurity and SES/store density and BMI and SES/store density in a diverse sample of adolescents and young adults across two studies in a large, urban city. GIS analysis revealed that participants with the highest food insecurity (larger symbols) tend to live in the zip codes with the lowest median income. There did not appear to be clear a relationship between food insecurity and store density. Participants with the highest BMI tend to live in zip codes with lower median income and participants with higher BMI tended to live further away from downtown, which has the highest concentration of grocery stores in the city. Our findings may help to inform future interventions and policy approaches to addressing both obesity and food insecurity in areas of higher prevalence.

This study will assess the link between various SES indicators and scientific and conspiracy beliefs as well as trust in science through a variety of socio-cognitive mediating variables.

The “Cognitive Electrophysiology in Socioeconomic Context in Adulthood” Dataset

Data Description

This dataset comprises electroencephalogram (EEG) data collected from 127 young adults (18-30 years), along with retrospective objective and subjective indicators of childhood family socioeconomic status (SES), as well as SES indicators in adulthood, such as educational attainment, individual and household income, food security, and home and neighborhood characteristics. The EEG data were recorded with tasks directly acquired from the Event-Related Potentials Compendium of Open Resources and Experiments ERP CORE (Kappenman et al., 2021), or adapted from these tasks (Isbell et al., 2024). These tasks, which are publicly available, were optimized to capture neural activity manifest in perception, cognition, and action, in neurotypical young adults. Furthermore, the dataset includes a symptoms checklist, consisting of questions that were found to be predictive of symptoms consistent with attention-deficit/hyperactivity disorder (ADHD) in adulthood, which can be used to investigate the links between ADHD symptoms and neural activity in a socioeconomically diverse young adult sample.

Notes

Before the data were publicly shared, all identifiable information was removed, including date of birth, race/ethnicity, zip code, and names of the languages the participants reported to speaking and understanding fluently. Date of birth was used to compute age in years, which is included in the dataset. The dataset consists of participants recruited for studies on adult cognition in context. To provide the largest sample size, we included all participants who completed at least one of the EEG tasks of interest. Each participant completed each EEG task only once. The original participant IDs with which the EEG data were saved were recoded and the raw EEG files were renamed to make the dataset BIDS compatible.

Copyright and License

This dataset is licensed under CC0.

References

Isbell, E., De León, N. E. R., & Richardson, D. M. (2024). Childhood family socioeconomic status is linked to adult brain electrophysiology. PloS One, 19(8), e0307406.

Kappenman, E. S., Farrens, J. L., Zhang, W., Stewart, A. X., & Luck, S. J. (2021). ERP CORE: An open resource for human event-related potential research. NeuroImage, 225, 117465.

Prevalence of normal weight, underweight, and overweight/obesity by socioeconomic status (SES) quintile, 2011 Bangladesh Health and Demographic Survey

Abstract

Kenya hosts over half a million refugees, who, along with their hosts in urban and camp areas, face difficult living conditions and limited socioeconomic opportunities. Most refugees in Kenya live in camps located in the impoverished counties of Turkana (40 percent) and Garissa (44 percent), while 16 percent inhabit urban areas—mainly in Nairobi but also in Mombasa and Nakuru.

Refugees in Kenya are not systematically included in national surveys, creating a lack of comparable socioeconomic data on camp-based and urban refugees, and their hosts. As the third of a series of surveys focusing on closing this gap, this Socioeconomic Survey of Urban Refugees's aim is to understand the socioeconomic needs of urban refugees in Kenya, especially in the face of ongoing conflicts, environmental hazards, and others shocks, as well as the recent government announcement to close Kenya’s refugee camps, which highlights the potential move of refugees from camps into urban settings.

The SESs are representative of urban refugees and camp-based refugees in Turkana County. For the Kalobeyei 2018 and Urban 2020–21 SESs, households were randomly selected from the UNHCR registration database (proGres), while a complete list of dwellings, obtained from UNHCR’s dwelling mapping exercise, was used to draw the sample for the Kakuma 2019 SES. The Kalobeyei SES and Kakuma SES were done via Computer-Assisted Personal Interviews (CAPI). Due to COVID-19 social distancing measures, the Urban SES was collected via Computer Assisted Telephone Interviewing (CATI). The Kalobeyei SES covers 6,004 households; the Kakuma SES covers 2,127 households; and the Urban SES covers 2,438 households in Nairobi, Nakuru, and Mombasa.

Questionnaires are aligned with national household survey instruments, while additional modules are added to explore refugee-specific dynamics. The SES includes modules on demographics, household characteristics, assets, employment, education, consumption, and expenditure, which are aligned with the Kenya Integrated Household Budget Survey (KIHBS) 2015–16 and the recent Kenya Continuous Household Survey (KCHS) 2019.

Additional modules on access to services, vulnerabilities, social cohesion, mechanisms for coping with lack of food, displacement trajectories, and durable solutions are administered to capture refugee-specific challenges.

Geographic coverage

Nairobi, Mombasa, Nakuru

Analysis unit

Households and individuals

Universe

All refugees registered with UNHCR via ProGres, verified via the Verification Exercise conducted in 2021

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was conducted using the UNHCR proGres data as the sampling frame. Due to the COVID-19 lockdown, the survey data was collected via telephone. Hence, the survey is representative of households with active phone numbers registered by UNHCR in urban Kenya – Nairobi, Mombasa and Nakuru. A sample size of 2,500 was needed to ensure a margin of error of less than 5 percent at a confidence level of 95 percent for groups represented by at least 50 percent of the population.

The sample for the urban SES is designed to estimate socioeconomic indicators, such as food insecurity, for groups whose share represents at least 50 percent of the population. Considering the total urban refugee population as of August 2020 and the proportions of main countries of origin, as well as a 10 percent nonresponse rate, the target sample size is 2,500 households in total, with 1,250 in Nairobi, 700 in Nakuru, and 550 in Mombasa. A total of 2,438 households were reached: 1,300 in Nairobi, 409 in Nakuru, and 729 in Mombasa.

The units in ProGres list are UNHCR proGres families, which are different from households as defined in standard household surveys. Upon registration, UNHCR groups individuals into ‘proGres’ families which do not necessarily meet the criteria to be considered a household. A proGres family is usually comprised by no more than one household. In turn, a household can be integrated by one or more proGres families.

Households were selected as the unit of observation to ensure comparability with national household surveys. Households are a set of related or unrelated people (either sharing the same dwelling or not) who pool ration cards and regularly cook and eat together. As proGres families were sampled, the identification of households was done by an introductory section that confirms that each member of the selected proGres family is a member of the household and whether there are other members in the households that belong to other ProGres families. Thus, the introductory section documents the number of proGres families present in the household under observation.

Before selecting the survey strata, the team attempted to better understand the type of bias observed by focusing on refugees with access to phones. From the proGres data, phone penetration in urban areas is high (Nairobi and Mombasa: 93 percent, Nakuru: 95 percent). To understand the type of bias observed by focusing on refugees with access to phone, we looked at socio-economic outcomes for proGres family refugees with access to a phone number and those without

Mode of data collection

Computer Assisted Telephone Interview [cati]