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.

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

IntroductionType 2 diabetes is increasing worldwide, and the trend is also observed in Sweden. In Malmö, the third largest city in Sweden, the prevalence has doubled. Populations with lower socioeconomic status have a higher prevalence and poorer outcomes, making preventive interventions targeting these groups increasingly important.ObjectiveTo investigate the types of interventions that have been tested and reported regarding the prevention of type 2 diabetes targeting low socioeconomic populations and are applicable in a high-income country.MethodsBased on a systematic search strategy developed using the People, Concept, and Context model, the databases CINAHL, PubMed, and Web of Science were searched in January 2024 and updated in December 2024, and EMBASE was searched in May 2025. A flowchart of the screening process has been created. From the selected studies, data were extracted, charted, and the findings were compiled in a narrative form.ResultsSeventeen studies were included, 12 were conducted in the United States and five in Europe. Most used culturally adapted diabetes prevention programs, and a higher proportion of participants were women. Key features included flexibility in attendance and format, development through a community-based participatory approach, gender-specific groups, and the involvement of significant others. Increases of physical activity proved challenging within broader lifestyle interventions. Screening interventions were conducted in community and healthcare facility settings, as well as through a school-and community-based program. Challenges with enrollment and retention were commonly reported.ConclusionThere is a need for more interventions in the European context and for interventions to engage more men with strategies such as male peer coaches and community screening in locations frequented by men. Longer time frames and sustained engagement strategies are necessary to reach and retain groups with low socioeconomic status in preventive type 2 diabetes interventions.

This data repository contains the research data from the study: Attitudes Toward Health, Healthcare, and eHealth of People With a Low Socioeconomic Status: A Community-Based Participatory Approach. The research was carried out from October 2019 to July 2020.

The research aimed to explore the attitudes of individuals with a low SEP towards health, healthcare, and eHealth. A mixed-method approach was employed, involving initial interviews and analysis using a grounded theory methodology to identify key themes. These themes were further validated and refined through an online questionnaire distributed to a separate sample of participants, followed by three focus groups to contextualize the findings.

Data analysis was performed using Atlas Ti for qualitative data and SPSS for quantitative data. The analysis for the quantitative data involved k-means cluster analysis, ANOVA tests, and a principal component analysis.

The dataset encompasses documents such as data management plans, ethics applications, consent forms, interview guides, and recruitment materials used throughout the study. It also includes qualitative themes derived from focus groups and questionnaires.

Ethical approval for the study was obtained from the Human Research Ethics Committee of the Delft University of Technology (approval numbers: 953, 1064, and 1141).

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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

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.

This service identifies U.S. Census Block Groups in which 51% or more of the households earn less than 80 percent of the Area Median Income (AMI). The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income.

Context: Currently it is not well understood to what extent there are obesity inequalities by socioeconomic status (SES) in urban Latin America. Objective: This study reviewed the literature assessing associations between overweight, obesity and SES in adults. Data sources: Pubmed and Scielo databases. Data extraction: Data extraction was conducted using the PRISMA guidelines. We extracted data on the direction of the association between SES (e.g. education and income), overweight (BMI ≥25 and <30 kg/m2) and obesity (BMI≥30 kg/m2) in Latin American urban regions. Relative differences between low and high SES groups were assessed and defined a priori as significant at p<0.05. Data analysis: Thirty-one studies met our inclusion criteria and most were conducted in Brazil (22) and Mexico. Only one study presented just non-significant associations. Fifty percent of associations between education or income and overweight were negative/inverse. Regarding obesity, 80% were negative and 20% positive. Most negative associations were found in women. Associations between BMI and SES usually followed the same pattern, except in men where they varied depending on the indicator used. Conclusion: Low SES individuals in urban Latin America, especially women, have higher BMI levels highlighting the need for interventions.

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.

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

Polygon geometry with attributes displaying the 2010 Census low income block groups in East Baton Rouge Parish, Louisiana.

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

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.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2020-2016

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.

Individual low-income status by low-income measure (before and after tax), age and gender for census metropolitan areas, tracted census agglomerations and census tracts.

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.

Russia Household Income: Year to Date: Group 2: 20% of Households with Low Income data was reported at 10.100 % in Dec 2018. This records a decrease from the previous number of 10.300 % for Sep 2018. Russia Household Income: Year to Date: Group 2: 20% of Households with Low Income data is updated quarterly, averaging 10.200 % from Mar 1995 (Median) to Dec 2018, with 96 observations. The data reached an all-time high of 11.300 % in Jun 1997 and a record low of 9.400 % in Mar 1999. Russia Household Income: Year to Date: Group 2: 20% of Households with Low Income data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HA012: Household Income Structure.