Background: The impact of socioeconomic differences on cancer survival has been investigated for several cancer types showing lower cancer survival in patients from lower socioeconomic groups. However, little is known about the relation between the strength of association and the level of adjustment and level of aggregation of the socioeconomic status measure. Here, we conduct the first systematic review and meta-analysis on the association of individual and area-based measures of socioeconomic status with lung cancer survival.Methods: In accordance with PRISMA guidelines, we searched for studies on socioeconomic differences in lung cancer survival in four electronic databases. A study was included if it reported a measure of survival in relation to education, income, occupation, or composite measures (indices). If possible, meta-analyses were conducted for studies reporting on individual and area-based socioeconomic measures.Results: We included 94 studies in the review, of which 23 measured socioeconomic status on an individual level and 71 on an area-based level. Seventeen studies were eligible to be included in the meta-analyses. The meta-analyses revealed a poorer prognosis for patients with low individual income (pooled hazard ratio: 1.13, 95 % confidence interval: 1.08–1.19, reference: high income), but not for individual education. Group comparisons for hazard ratios of area-based studies indicated a poorer prognosis for lower socioeconomic groups, irrespective of the socioeconomic measure. In most studies, reported 1-, 3-, and 5-year survival rates across socioeconomic status groups showed decreasing rates with decreasing socioeconomic status for both individual and area-based measures. We cannot confirm a consistent relationship between level of aggregation and effect size, however, comparability across studies was hampered by heterogeneous reporting of socioeconomic status and survival measures. Only eight studies considered smoking status in the analysis.Conclusions: Our findings suggest a weak positive association between individual income and lung cancer survival. Studies reporting on socioeconomic differences in lung cancer survival should consider including smoking status of the patients in their analysis and to stratify by relevant prognostic factors to further explore the reasons for socioeconomic differences. A common definition for socioeconomic status measures is desirable to further enhance comparisons between nations and across different levels of aggregation.

This dataset provides Census 2021 estimates that classify Household Reference Persons aged 16 years and over in England and Wales by NS-SEC of Household Reference Person and by household composition. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Data about household relationships might not always look consistent with legal partnership status. This is because of complexity of living arrangements and the way people interpreted these questions. Take care when using these two variables together. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Household composition

Households according to the relationships between members.

One-family households are classified by:

• the number of dependent children
• family type (married, civil partnership or cohabiting couple family, or lone parent family)

Other households are classified by:

• the number of people
• the number of dependent children
• whether the household consists only of students or only of people aged 66 and over

Between 2019 and 2023, people living in households in the Asian and ‘Other’ ethnic groups were most likely to be in persistent low income before and after housing costs

Additional file of Development of the Global Network for Women’s and Children’s Health Research’s socioeconomic status index for use in the network’s sites in low and lower middle-income countries

AimsTo systematically review the evidence of socioeconomic inequalities for adults with type 1 diabetes in relation to mortality, morbidity and diabetes management.MethodsWe carried out a systematic search across six relevant databases and included all studies reporting associations between socioeconomic indicators and mortality, morbidity, or diabetes management for adults with type 1 diabetes. Data extraction and quality assessment was undertaken for all included studies. A narrative synthesis was conducted.ResultsA total of 33 studies were identified. Twelve cohort, 19 cross sectional and 2 case control studies met the inclusion criteria. Regardless of healthcare system, low socioeconomic status was associated with poorer outcomes. Following adjustments for other risk factors, socioeconomic status was a statistically significant independent predictor of mortality in 9/10 studies and morbidity in 8/10 studies for adults with type 1 diabetes. There appeared to be an association between low socioeconomic status and some aspects of diabetes management. Although only 3 of 16 studies made adjustments for confounders and other risk factors, poor diabetes management was associated with lower socioeconomic status in 3/3 of these studies.ConclusionsLow socioeconomic status is associated with higher levels of mortality and morbidity for adults with type 1 diabetes even amongst those with access to a universal healthcare system. The association between low socioeconomic status and diabetes management requires further research given the paucity of evidence and the potential for diabetes management to mitigate the adverse effects of low socioeconomic status.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by economic activity status. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Economic activity status

People aged 16 years and over are economically active if, between 15 March and 21 March 2021, they were:

• in employment (an employee or self-employed)
• unemployed, but looking for work and could start within two weeks
• unemployed, but waiting to start a job that had been offered and accepted

It is a measure of whether or not a person was an active participant in the labour market during this period. Economically inactive are those aged 16 years and over who did not have a job between 15 March to 21 March 2021 and had not looked for work between 22 February to 21 March 2021 or could not start work within two weeks.

The census definition differs from International Labour Organization definition used on the Labour Force Survey, so estimates are not directly comparable.

This classification splits out full-time students from those who are not full-time students when they are employed or unemployed. It is recommended to sum these together to look at all of those in employment or unemployed, or to use the four category labour market classification, if you want to look at all those with a particular labour market status.

BackgroundChildren from families with low socioeconomic status (SES), as determined by income, experience several negative outcomes, such as higher rates of newborn mortality and behavioral issues. Moreover, associations between DNA methylation and low income or poverty status are evident beginning at birth, suggesting prenatal influences on offspring development. Recent evidence suggests neighborhood opportunities may protect against some of the health consequences of living in low income households. The goal of this study was to assess whether neighborhood opportunities moderate associations between household income (HI) and neonate developmental maturity as measured with DNA methylation.MethodsUmbilical cord blood DNA methylation data was available in 198 mother-neonate pairs from the larger CANDLE cohort. Gestational age acceleration was calculated using an epigenetic clock designed for neonates. Prenatal HI and neighborhood opportunities measured with the Childhood Opportunity Index (COI) were regressed on gestational age acceleration controlling for sex, race, and cellular composition.ResultsHigher HI was associated with higher gestational age acceleration (B = .145, t = 4.969, p = 1.56x10-6, 95% CI [.087, .202]). Contrary to expectation, an interaction emerged showing higher neighborhood educational opportunity was associated with lower gestational age acceleration at birth for neonates with mothers living in moderate to high HI (B = -.048, t = -2.08, p = .03, 95% CI [-.092, -.002]). Female neonates showed higher gestational age acceleration at birth compared to males. However, within males, being born into neighborhoods with higher social and economic opportunity was associated with higher gestational age acceleration.ConclusionPrenatal HI and neighborhood qualities may affect gestational age acceleration at birth. Therefore, policy makers should consider neighborhood qualities as one opportunity to mitigate prenatal developmental effects of HI.

This dataset contains interview transcriptions of interviews with 13 GPs on their experiences with communication with patients from different cultural backgrounds and/or low socio-economic status

The database with the variables used in the study. The codebook of the database.

We aimed to estimate the prevalence of high body adiposity and its association with musculoskeletal fitness in male children and adolescents according to socioeconomic status. A cross-sectional epidemiological study was carried out with 1,531 school children (6-17 years old) attending public schools in Cascavel, state of Paraná, Brazil. Body adiposity was estimated based on skinfold thickness. Information was also collected on chronological age, socioeconomic status, sexual maturation, performance in physical tests such as sit and reach, 1-minute sit-up, stationary long jump and Shuttle run. Statistical analyses were performed (Student's unpaired t test and Poisson regression) taking into consideration socioeconomic status (high and low+middle), with p<0.05. High body adiposity was observed in 30.4% of the sample, and was greater (p<0.05) among those of high socioeconomic status (33.3% vs 28.3%). After adjustment for all variables, high body adiposity was associated with low abdominal resistance (PR=1.44; CI95%=1.05-1.99) and lower limb power (PR=2.09; CI95%=1.46-1.98) in the low socioeconomic status group. In the high socioeconomic status group, the outcome was associated with low abdominal resistance (PR=1.72; CI95%=1.17-2.51) and with intermediate (PR=2.83; CI95%=1.76-4.55) and low (PR=3.90; CI95%=2.38-6.38) lower limb power. In both socioeconomic levels, lower musculoskeletal fitness (abdominal resistance and lower limb power) was associated with high body adiposity. However, the magnitude of the association between muscular capacity and high body adiposity seems to differ according to socioeconomic status.

The following report outlines the workflow used to optimize your Find Outliers result:Initial Data Assessment.There were 1684 valid input features.POVERTY Properties:Min0.0000Max91.8000Mean18.9902Std. Dev.12.7152There were 22 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 3709.0000 Meters.Outlier AnalysisCreating the random reference distribution with 499 permutations.There are 1155 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.There are 68 statistically significant high outlier features.There are 84 statistically significant low outlier features.There are 557 features part of statistically significant low clusters.There are 446 features part of statistically significant high clusters.OutputPink output features are part of a cluster of high POVERTY values.Light Blue output features are part of a cluster of low POVERTY values.Red output features represent high outliers within a cluster of low POVERTY values.Blue output features represent low outliers within a cluster of high POVERTY values.

This dataset was developed to evaluate the preventative integrated e-health approach (PIE-approach). For more detailed information, please refer to the associated publication (Israel, van Lenthe & Beenackers, 2025). The PIE approach was specifically aimed at individuals with a lower socioeconomic position, who therefore comprise the majority of the sample. The dataset includes data from both participants in the intervention (n=85) and individuals in a control group (n=610) who were surveyed for comparison. Participants completed up to three questionnaires: one at baseline, a second approximately three months later, and a third approximately twelve months after the initial assessment. Inclusion was over the course of several years.

This study aimed to measure changes in socioeconomic inequalities in smoking and smoking cessation due to the 2006 smoking ban in Luxembourg. Data were derived from the PSELL3/EU-SILC (Panel Socio-Economique Liewen Zu Letzebuerg/European Union—Statistic on Income and Living Conditions) survey, which was a representative survey of the general population aged ≥16 years conducted in Luxembourg in 2005, 2007, and 2008. Smoking prevalence and smoking cessation due to the 2006 smoking ban were used as the main smoking outcomes. Two inequality measures were calculated to assess the magnitude and temporal trends of socioeconomic inequalities in smoking: the prevalence ratio and the disparity index. Smoking cessation due to the smoking ban was considered as a positive outcome. Three multiple logistic regression models were used to assess social inequalities in smoking cessation due to the 2006 smoking ban. Education level, income, and employment status served as proxies for socioeconomic status. The prevalence of smoking decreased by 22.5% between 2005 and 2008 (from 23.1% in 2005 to 17.9% in 2008), but socioeconomic inequalities in smoking persisted. Smoking prevalence decreased by 24.2% and 20.2% in men and women, respectively; this difference was not statistically significant. Smoking cessation in daily smokers due to the 2006 smoking ban was associated with education level, employment status, and income, with higher percentages of quitters among those with a lower socioeconomic status. The decrease in smoking prevalence after the 2006 law was also associated with a reduction in socioeconomic inequalities, including differences in education level, income, and employment status. Although the smoking ban contributed to a reduction of such inequalities, they still persist, indicating the need for a more targeted approach of smoke-free policies directed toward lower socioeconomic groups.

As of July 2024, roughly 40.4 percent of respondents claimed to belong to the middle class, followed by the lower class or poor at nearly 20 percent. Another 14 percent of respondents said they were lower middle class.

This dataset contains estimates of the socioeconomic status (SES) position of each of 149 countries covering the period 1880-2010. Measures of SES, which are in decades, allow for a 130 year time-series analysis of the changing position of countries in the global status hierarchy. SES scores are the average of each country’s income and education ranking and are reported as percentile rankings ranging from 1-99. As such, they can be interpreted similarly to other percentile rankings, such has high school standardized test scores. If country A has an SES score of 55, for example, it indicates that 55 percent of the countries in this dataset have a lower average income and education ranking than country A. ISO alpha and numeric country codes are included to allow users to merge these data with other variables, such as those found in the World Bank’s World Development Indicators Database and the United Nations Common Database.

See here for a working example of how the data might be used to better understand how the world came to look the way it does, at least in terms of status position of countries.

VARIABLE DESCRIPTIONS:

unid: ISO numeric country code (used by the United Nations)

wbid: ISO alpha country code (used by the World Bank)

SES: Country socioeconomic status score (percentile) based on GDP per capita and educational attainment (n=174)

country: Short country name

year: Survey year

gdppc: GDP per capita: Single time-series (imputed)

yrseduc: Completed years of education in the adult (15+) population

region5: Five category regional coding schema

regionUN: United Nations regional coding schema

DATA SOURCES:

The dataset was compiled by Shawn Dorius (sdorius@iastate.edu) from a large number of data sources, listed below. GDP per Capita:

1. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. GDP & GDP per capita data in (1990 Geary-Khamis dollars, PPPs of currencies and average prices of commodities). Maddison data collected from: http://www.ggdc.net/MADDISON/Historical_Statistics/horizontal-file_02-2010.xls.

2. World Development Indicators Database Years of Education 1. Morrisson and Murtin.2009. 'The Century of Education'. Journal of Human Capital(3)1:1-42. Data downloaded from http://www.fabricemurtin.com/ 2. Cohen, Daniel & Marcelo Cohen. 2007. 'Growth and human capital: Good data, good results' Journal of economic growth 12(1):51-76. Data downloaded from http://soto.iae-csic.org/Data.htm

3. Barro, Robert and Jong-Wha Lee, 2013, "A New Data Set of Educational Attainment in the World, 1950-2010." Journal of Development Economics, vol 104, pp.184-198. Data downloaded from http://www.barrolee.com/

4. Maddison, Angus. 2004. 'The World Economy: Historical Statistics'. Organization for Economic Co-operation and Development: Paris. 13.

5. United Nations Population Division. 2009.

ABSTRACT OBJECTIVE: To analyze the moderator effect of socioeconomic status in the association between the perceived environment and active commuting to school. METHODS: A total of 495 adolescents and their parents were interviewed. Perceived environment was operationalized in traffic and crime safety and assessed with the Neighborhood Environment Walkability Scale. Active commuting was self-reported by the adolescents, categorized in walking, bicycling or skating at least one time/week. Socioeconomic status was used as moderator effect, reported from adolescents' parents or guardians using Brazilian standardized socioeconomic status classification. Analyses were performed with Poisson regression on Stata 12.0. RESULTS: Prevalence of active commuting was 63%. Adolescents with low socioeconomic status who reported “it is easy to observe pedestrians and cyclists” were more likely to actively commute to school (PR = 1.18, 95%CI 1.03–1.13). Adolescents with low socioeconomic status whose parents or legal guardians reported positively to “being safe crossing the streets” had increased probability of active commuting to school (PR = 1.10, 95%CI 1.01–1.20), as well as those with high socioeconomic status with “perception of crime” were positively associated to the outcome (PR = 1.33, 95%CI 1.03–1.72). CONCLUSIONS: Socioeconomic status showed moderating effects in the association between the perceived environment and active commuting to school.

Abstract The aim of the study was to investigate the influence of clinical and socioeconomic factors on social capital throughout adolescence. A cohort study was performed in 2012 (T1) with a random sample of 1,134 12-year-old adolescents from Santa Maria, Brazil. Questions on socioeconomic factors (maternal education, household income, household crowding) were answered by the parents. Clinicians evaluated their dental caries (decayed, missing, and filled status of permanent teeth) and gingival bleeding (using the Community Periodontal Index). Contextual variables including the mean income of the neighborhood in which the school was located were used (T1). The adolescents were revaluated in 2018 (T2) and answered questions regarding social capital (social trust, social control, empowerment, neighborhood security, and political effectiveness). A path analysis was used to test the relationship between the predictor variables (T1) and social capital (T2). A total of 768 adolescents were reevaluated at a 6-year follow-up (cohort retention rate of 67.7%). Most of the adolescents were girls, with a low household income, about 40% had caries experience (T1), and about 64% had high social capital (T2). The highest neighborhood’s mean income was related to a lower household income in T1 (p < 0.01), and this was directly related to a low social capital in T2 (p = 0.04). Furthermore, caries experience at T1 was directly associated with low social capital at T2 (p = 0.03). Socioeconomic factors were also related to caries experience. Individuals who lived in neighborhoods with greater inequality such as families with a low household income and those with untreated dental caries in early adolescence, had a low social capital after follow-up.

Objective: To determine whether lower socioeconomic status (SES) and longer home-hospital driving time are associated with reductions in tPA administration and timeliness of the treatment. Methods: We conducted a retrospective observational study using data from the Get With The Guidelines-Stroke Registry (GWTG-Stroke) between January 2015 to March 2017. The study included 118,683 ischemic stroke patients age ≥18 who were transported by EMS to one of 1,489 US hospitals. We defined each patient’s SES based on their zip code median household income. We calculated the driving time between each patient’s home zip code and the hospital where they were treated, using the Google Maps Directions Application Programing Interface. The primary outcomes were tPA administration and onset-to-arrival time (OTA). Outcomes were analyzed using hierarchical multivariable logistic regression models. Results: SES was not associated with OTA (p=0.31) or tPA administration (p=0.47), but was associated with the secondary outcomes of onset-to-treatment time (p=0.0160) and in-hospital mortality (p=0.0037), with higher SES associated with shorter OTT and lower in-hospital mortality. Driving time was associated with tPA administration (p <0.001) and OTA (p <0.0001), with lower odds of tPA (0.83, 0.79-0.88) and longer OTA (1.30, 1.24-1.35) in patients with the longest versus shortest driving time quartiles. Lower SES quintiles were associated with slightly longer driving time quartiles (p=0.0029), but there was no interaction between the SES and driving time for either OTA (p=0.1145) or tPA (p=0.6103). Conclusions: Longer driving times were associated with lower odds of tPA administration and longer OTA, however SES did not modify these associations.

Data used in the article One but not two grandmothers increased child survival in poorer families in west Bohemian population

Human childrearing is characterized by cooperative care and grandmothers are usually the most prominent alloparents. Nevertheless, it has been argued that limited resources may intensify competition among kin. The effect of grandmothers’ presence on child survival may thus crucially depend on the family’s socioeconomic status. We evaluate the impact of grandmothers’ presence on child survival using a large historical dataset from eighteenth to nineteenth-century western Bohemia (N = 6880) and assess the effects of socioeconomic status. We employed a varying effects model conditioned on relatedness between individuals because of possible genetically transmitted benefits. Proportional hazards showed that grandmothers had little or no impact on child survival in families of high and medium socioeconomic status (farmers and cottagers, respectively), while in families with the lowest socioeconomic status (lodgers), grandmothers’ presence increased the survival probability of children up to five years of age. The beneficial effect of grandmaternal care was strongest between the first and second year of life. Importantly, though, in families with low socioeconomic status, we also observed lower survival chances of children when both grandmothers lived in the same village. These findings suggest that the balance between kin cooperation in childrearing and competition over resources may depend on resource availability.

Methods The data were digitalized from historical curch records by a group of experts led by Alice Velková. The parent-children pairs were identified on the basis of names, residence sites and dates of births/deaths. Incomplete or corrupted data were excluded. Details can be found in the manuscript or on a GitHub page: https://github.com/costlysignalling/CharlieBucket See attached scripts for the complete analysis and data handling record.

The aim of this questionnaire is to examine correlations among socioeconomic status, perceived stigmatisation, general shame and social inadequacy, and poor health.