This table presents income shares, thresholds, tax shares, and total counts of individual Canadian tax filers, with a focus on high income individuals (95% income threshold, 99% threshold, etc.). Income thresholds are based on national threshold values, regardless of selected geography; for example, the number of Nova Scotians in the top 1% will be calculated as the number of taxfiling Nova Scotians whose total income exceeded the 99% national income threshold. Different definitions of income are available in the table namely market, total, and after-tax income, both with and without capital gains.

In March 2025, the top one percent of earners in the United Kingdom received an average pay of over 16,000 British pounds per month, compared with the bottom ten percent of earners who earned around 800 pounds a month.

BackgroundHealthy Lifestyle Centres (HLCs) are state-owned, free-of-charge facilities that screen for major noncommunicable disease risks and promote healthy lifestyles among adults older than 35 years in Sri Lanka. The key challenge to their effectiveness is their underutilisation. This study aimed to describe the underutilisation and determine the factors associated, as a precedent of a bigger project that designed and implemented an intervention for its improvement.MethodsData derived from a community-based cross-sectional study conducted among 1727 adults (aged 35 to 65 years) recruited using a multi-stage cluster sampling method from two districts (Gampaha and Kalutara) in Sri Lanka. A prior qualitative study was used to identify potential factors to develop the questionnaire which is published separately. Data were obtained using an interviewer-administered questionnaire and analysed using inferential statistics.ResultsForty-two percent (n = 726, 95% CI: 39.7–44.4) had a satisfactory level of awareness on HLCs even though utilisation was only 11.3% (n = 195, 95% CI: 9.80–12.8). Utilisation was significantly associated with 14 factors. The five factors with the highest Odds Ratios (OR) were perceiving screening as useful (OR = 10.2, 95% CI: 4.04–23.4), perceiving as susceptible to NCDs (OR = 6.78, 95% CI: 2.79–16.42) and the presence of peer support for screening and a healthy lifestyle (OR = 3.12, 95% CI: 1.54–6.34), belonging to the second (OR = 3.69, 95% CI: 1.53–8.89) and third lowest (OR = 2.84, 95% CI: 1.02–7.94) household income categories and a higher level of knowledge on HLCs (OR = 1.31, 95% CI: 1.24–1.38). When considering non-utilisation, being a male (OR = 0.18, 95% CI: 0.05–0.52), belonging to an extended family (OR = 0.43, 95% CI: 0.21–0.88), residing within 1–2 km (OR = 0.29, 95% CI: 0.14–0.63) or more than 3 km of the HLC (OR = 0.14, 95% CI: 0.04–0.53), having a higher self-assessed health score (OR = 0.97, 95% CI: 0.95–0.99) and low perceived accessibility to HLCs (OR = 0.12, 95% CI: 0.04–0.36) were significantly associated.ConclusionIn conclusion, underutilisation of HLCs is a result of multiple factors operating at different levels. Therefore, interventions aiming to improve HLC utilisation should be complex and multifaceted designs based on these factors rather than merely improving knowledge.

Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

IntroductionUnderstanding how socioeconomic markers interact could inform future policies aimed at increasing adherence to a healthy diet.MethodsThis cross-sectional study included 437,860 participants from the UK Biobank. Dietary intake was self-reported. Were used as measures socioeconomic education level, income and Townsend deprivation index. A healthy diet score was defined using current dietary recommendations for nine food items and one point was assigned for meeting the recommendation for each. Good adherence to a healthy diet was defined as the top 75th percentile, while poor adherence was defined as the lowest 25th percentile. Poisson regression was used to investigate adherence to dietary recommendations.ResultsThere were significant trends whereby diet scores tended to be less healthy as deprivation markers increased. The diet score trends were greater for education compared to area deprivation and income. Compared to participants with the highest level of education, those with the lowest education were found to be 48% less likely to adhere to a healthy diet (95% Confidence Interval [CI]: 0.60–0.64). Additionally, participants with the lowest income level were 33% less likely to maintain a healthy diet (95% CI: 0.73–0.81), and those in the most deprived areas were 13% less likely (95% CI: 0.84–0.91).Discussion/conclussionAmong the three measured proxies of socioeconomic status – education, income, and area deprivation – low education emerged as the strongest factor associated with lower adherence to a healthy diet.

BackgroundAlthough most Indians live in rural settings, data on cardiovascular disease risk factors in these groups are limited. We describe the association between socioeconomic position and cardiovascular disease risk factors in a large rural population in north India.MethodsWe performed representative, community-based sampling from 2013 to 2014 of Solan district in Himachal Pradesh. We used education, occupation, household income, and household assets as indicators of socioeconomic position. We used tobacco use, alcohol use, low physical activity, obesity, hypertension, and diabetes as risk factors for cardiovascular disease. We performed hierarchical multivariable logistic regression, adjusting for age, sex and clustering of the health sub-centers, to evaluate the cross-sectional association of socioeconomic position indicators and cardiovascular disease risk factors.ResultsAmong 38,457 participants, mean (SD) age was 42.7 (15.9) years, and 57% were women. The odds of tobacco use was lowest in participants with graduate school and above education (adjusted OR 0.11, 95% CI 0.09, 0.13), household income >15,000 INR (adjusted OR 0.35, 95% CI 0.29, 0.43), and highest quartile of assets (adjusted OR 0.28, 95% CI 0.24, 0.34) compared with other groups but not occupation (skilled worker adjusted OR 0.93, 95% CI 0.74, 1.16). Alcohol use was lower among individuals in the higher quartile of income (adjusted OR 0.75, 95% CI 0.64, 0.88) and assets (adjusted OR 0.70, 95% CI 0.59, 0.82). The odds of obesity was highest in participants with graduate school and above education (adjusted OR 2.33, 95% CI 1.85, 2.94), household income > 15,000 Indian rupees (adjusted OR 1.89, 95% CI 1.63, 2.19), and highest quartile of household assets (adjusted OR 2.87, 95% CI 2.39, 3.45). The odds of prevalent hypertension and diabetes were also generally higher among individuals with higher socioeconomic position.ConclusionsIndividuals with lower socioeconomic position in Himachal Pradesh were more likely to have abnormal behavioral risk factors, and individuals with higher socioeconomic position were more likely to have abnormal clinical risk factors.

BackgroundAdult height reflects childhood circumstances and is associated with health, longevity, and maternal–fetal outcomes. Mean height is an important population metric, and declines in height have occurred in several low- and middle-income countries, especially in Africa, over the last several decades. This study examines changes at the population level in the distribution of height over time across a broad range of low- and middle-income countries during the past half century.Methods and findingsThe study population comprised 1,122,845 women aged 25–49 years from 59 countries with women’s height measures available from four 10-year birth cohorts from 1950 to 1989 using data from the Demographic and Health Surveys (DHS) collected between 1993 and 2013. Multilevel regression models were used to examine the association between (1) mean height and standard deviation (SD) of height (a population-level measure of inequality) and (2) median height and the 5th and 95th percentiles of height. Mean-difference plots were used to conduct a graphical analysis of shifts in the distribution within countries over time. Overall, 26 countries experienced a significant increase, 26 experienced no significant change, and 7 experienced a significant decline in mean height between the first and last birth cohorts. Rwanda experienced the greatest loss in height (−1.4 cm, 95% CI: −1.84 cm, −0.96 cm) while Colombia experienced the greatest gain in height (2.6 cm, 95% CI: 2.36 cm, 2.84 cm). Between 1950 and 1989, 24 out of 59 countries experienced a significant change in the SD of women’s height, with increased SD in 7 countries—all of which are located in sub-Saharan Africa. The distribution of women’s height has not stayed constant across successive birth cohorts, and regression models suggest there is no evidence of a significant relationship between mean height and the SD of height (β = 0.015 cm, 95% CI: −0.032 cm, 0.061 cm), while there is evidence for a positive association between median height and the 5th percentile (β = 0.915 cm, 95% CI: 0.820 cm, 1.002 cm) and 95th percentile (β = 0.995 cm, 95% CI: 0.925 cm, 1.066 cm) of height. Benin experienced the largest relative expansion in the distribution of height. In Benin, the ratio of variance between the latest and earliest cohort is estimated as 1.5 (95% CI: 1.4, 1.6), while Lesotho and Uganda experienced the greatest relative contraction of the distribution, with the ratio of variance between the latest and earliest cohort estimated as 0.8 (95% CI: 0.7, 0.9) in both countries. Limitations of the study include the representativeness of DHS surveys over time, age-related height loss, and consistency in the measurement of height between surveys.ConclusionsThe findings of this study indicate that the population-level distribution of women’s height does not stay constant in relation to mean changes. Because using mean height as a summary population measure does not capture broader distributional changes, overreliance on the mean may lead investigators to underestimate disparities in the distribution of environmental and nutritional determinants of health.

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on student access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average".The Canada Millennium Scholarship Foundation commissioned R.A. Malatest and Associates Ltd. to conduct a comprehensive survey that provided national-level data concerning college students’ income, expenditures, levels of debt/perceptions of debt, and use of time. The 2002 Canadian College Student Survey Project was administered in March and April of 2002 in 16 colleges (representing 93,175 students). The maximum variation of the results of this survey is estimated to be ±1.2% (at a 95% confidence level). This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

This table contains 1408 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...), Age group (2 items: At birth; At age 65 ...), Sex (2 items: Males; Females ...), Income group (4 items: All income groups; Income group; tercile 1 (lowest);Income group; tercile 3 (highest);Income group; tercile 2 (middle) ...), Characteristics (8 items: Health-adjusted life expectancy; Low 95% confidence interval; health-adjusted life expectancy; Coefficient of variation for health-adjusted life expectancy; High 95% confidence interval; health-adjusted life expectancy ...).

Content

A dataset of counties that are representative for Germany with regard to

• the average disposable income,
• the quota of divorces,
• the respective quotas of employees working in the services (excluding logistics, security, and cleaning) and the MINT sectors,
• the proportions of age groups in the total proportion of the respective population, with age groups in five-year strata for the population aged between 30 and 65 and the population in the age range between 65 and 75 each considered separately for the calculation of representativeness.
• population density, if the benchmark is defined as the national average density weighted by county population. This value is calculated by averaging the population-per-square-kilometer figures of all German counties, weighted by the population size of each county. In contrast to a simple arithmetic mean of county-level densities, which would disproportionately reflect sparsely populated areas, this method reflects the actual density of the environments where people live. The resulting value for the selected counties falls within the 95% confidence interval of the national weighted average.

In addition, data from the four big cities Berlin, München (Munich), Hamburg, and Köln (Cologne) were collected and reflected in the dataset.

The dataset is based on the most recent data available at the time of the creation of the dataset, mainly deriving from 2022, as set out in detail in the readme.md file.

Method applied

The selection of the representative counties, as reflected in the dataset, was performed on the basis of official statistics with the aim of obtaining a confidence rate of 95%. The selection was based on a principal component analysis of the statistical data available for Germany and the addition of the regions with the lowest population density and the highest and lowest per capita disposable income. A check of the representativity of the selected counties was performed.

In the case of Leipzig, the city and the district had to be treated together, in deviation from the official territorial division, with respect to a specific use case of the data.

BackgroundPrior research suggests that United States governmental sources documenting the number of law-enforcement-related deaths (i.e., fatalities due to injuries inflicted by law enforcement officers) undercount these incidents. The National Vital Statistics System (NVSS), administered by the federal government and based on state death certificate data, identifies such deaths by assigning them diagnostic codes corresponding to “legal intervention” in accordance with the International Classification of Diseases–10th Revision (ICD-10). Newer, nongovernmental databases track law-enforcement-related deaths by compiling news media reports and provide an opportunity to assess the magnitude and determinants of suspected NVSS underreporting. Our a priori hypotheses were that underreporting by the NVSS would exceed that by the news media sources, and that underreporting rates would be higher for decedents of color versus white, decedents in lower versus higher income counties, decedents killed by non-firearm (e.g., Taser) versus firearm mechanisms, and deaths recorded by a medical examiner versus coroner.Methods and findingsWe created a new US-wide dataset by matching cases reported in a nongovernmental, news-media-based dataset produced by the newspaper The Guardian, The Counted, to identifiable NVSS mortality records for 2015. We conducted 2 main analyses for this cross-sectional study: (1) an estimate of the total number of deaths and the proportion unreported by each source using capture–recapture analysis and (2) an assessment of correlates of underreporting of law-enforcement-related deaths (demographic characteristics of the decedent, mechanism of death, death investigator type [medical examiner versus coroner], county median income, and county urbanicity) in the NVSS using multilevel logistic regression. We estimated that the total number of law-enforcement-related deaths in 2015 was 1,166 (95% CI: 1,153, 1,184). There were 599 deaths reported in The Counted only, 36 reported in the NVSS only, 487 reported in both lists, and an estimated 44 (95% CI: 31, 62) not reported in either source. The NVSS documented 44.9% (95% CI: 44.2%, 45.4%) of the total number of deaths, and The Counted documented 93.1% (95% CI: 91.7%, 94.2%). In a multivariable mixed-effects logistic model that controlled for all individual- and county-level covariates, decedents injured by non-firearm mechanisms had higher odds of underreporting in the NVSS than those injured by firearms (odds ratio [OR]: 68.2; 95% CI: 15.7, 297.5; p < 0.01), and underreporting was also more likely outside of the highest-income-quintile counties (OR for the lowest versus highest income quintile: 10.1; 95% CI: 2.4, 42.8; p < 0.01). There was no statistically significant difference in the odds of underreporting in the NVSS for deaths certified by coroners compared to medical examiners, and the odds of underreporting did not vary by race/ethnicity. One limitation of our analyses is that we were unable to examine the characteristics of cases that were unreported in The Counted.ConclusionsThe media-based source, The Counted, reported a considerably higher proportion of law-enforcement-related deaths than the NVSS, which failed to report a majority of these incidents. For the NVSS, rates of underreporting were higher in lower income counties and for decedents killed by non-firearm mechanisms. There was no evidence suggesting that underreporting varied by death investigator type (medical examiner versus coroner) or race/ethnicity.

This table contains 1408 series, with data for years 2001 - 2001 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...), Age group (2 items: At birth; At age 65 ...), Sex (2 items: Males; Females ...), Income group (4 items: All income groups; Income group; tercile 1 (lowest);Income group; tercile 3 (highest);Income group; tercile 2 (middle) ...), Characteristics (8 items: Health-adjusted life expectancy; Low 95% confidence interval; health-adjusted life expectancy; Coefficient of variation for health-adjusted life expectancy; High 95% confidence interval; health-adjusted life expectancy ...).

Beta Coefficients, 95% Confidence Interval, and Statistical Significance for County-Level Economic Variables Using Linear Regression with Prevalence of Poor Mental Health as the Dependent Variable, Overall and by Urban/Rural Classification, United States, 2019. Blue-filled cells indicate a positive association between the variable and the dependent variable; red-filled cells indicate a negative association; greyed out cells indicate the variable was not significant; blank cells indicate a variable that was not included in the model.

A household survey (cross sectional study) was conducted to establish the consumption of fish, fish products and other food items at household level (N=714). The role of fish and fish products in the diets of urban poor households, and how fish consumption is distributed within the household between women, children and men. Women and children in the first 1,000 days of life were specifically targeted. Children aged 24 – 59 months from participating households were also enrolled in the study. Lusaka district in Lusaka Province was purposively selected as the study area for the following reasons: it is an urban area within Lusaka Province with the highest number of high density settlement townships where the majority of the urban poor live in Zambia. The study targeted low-income settlement localities as the people living in these areas are most vulnerable to food and nutrition insecurity. To derive the sample size, the formula was applied; n is the minimum required sample size, Z is the Z score for the desired level of confidence (assumed to be 95% or = 0.05), is the population proportion of interest estimated to be 11%, the prevalence of stunted growth among children in Lusaka (27) and d is the margin of error (assumed to be 5%). The calculated sample size was further adjusted for the design effect and non-response rate (predicted to be 5%), to obtain the optimal sample size of 714 households. A sampling frame was developed from the 2010 Population Census and Housing report, in consultation with the local authorities and the Central Statistics Office (CSO). The sampling process involved, firstly, purposively selecting the three constituencies (Kanyama, Matero and Munali) from Lusaka district. From each constituency, one ward was randomly selected to participate in the study. In each reporting domain, study households were selected using a three-stage randomized cluster approach, with the first two stages using the Ward and Standard Enumeration Area (SEA) sampling frame from the 2010 CSO. A total of 36 SEAs (clusters) were identified and from each, 20 households were selected. Using a determined sampling interval, systematic random sampling was used in the final sampling stage. Primary data collection was carried out through a tablet-based questionnaire and by the use of the KoBo Toolkit, a platform to customise the survey to collect specific data, in this study: a) Demographic and socio-economic characteristics, including employment and income generating activities, water and sanitation, and household assets; b) Dietary diversity questionnaires were developed and used to collect dietary data for children, women and men. Guidelines on food groups to be included in the questionnaire as provided by FAO 2013 were used in developing the questionnaire for women, men and for household level data collection. The WHO 2010 guidelines were used in developing the questionnaire for collecting dietary data for children 6–23 months of age. Dietary diversity is a proxy for adequate micronutrient-density of foods. A 24 hour recall collected data that was used to estimate food intake for two adults within the household (one male and one female), infants aged 6 – 23 months and one child aged 2 – 5 years. Development of the 24 hr recall was based on the methods described by Gibson and Ferguson (2008). In addition, a dietary diversity questionnaire (FFQ) was used collect data on various food groups women, children and men consumed in the last 24 hours prior to the study. With focus on fish in the diet of young children, information was collected on the use of fish in the initiation of complementary feeding, the age at which fish is fed to children, the perceptions of mother and fathers of the importance of fish for growth and development of the young child. c) Anthropometric measurements such as weight and length/height were taken on the children and mothers/caregivers. This was done to enable determine the nutritional status of children 6 -23 months; 24- 59 months and women aged 19 – 49 years. The weights of children were taken using the SECA electronic scale and for those children, who were unable to stand, the parents/guardians were asked to carry them and their weights were subtracted from the mothers’ weight. The children’s weights were taken to the nearest 0.1 kg with minimal clothes on them. Length/height boards were used to take the length/height to the nearest 0.1 cm. Children’s age was verified using the clinic card. The mothers’ weight and height were also taken using the SECA scales. The measurements were used to determine mothers’ BMI.

This table contains 58320 series, with data for years 1999 - 2016 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (20 items: Canada; Atlantic; Newfoundland and Labrador; Prince Edward Island; ...); Assets and debts (27 items: Total assets; Private pension assets; Registered Retirement Savings Plans (RRSPs), Registered Retirement Income Funds (RRIFs), Locked-in Retirement Accounts (LIRAs) and other; Employer-sponsored Registered Pension Plans (EPPs); ...); Net worth quintiles (6 items: Total, all net worth quintiles; Lowest net worth quintile; Second net worth quintile; Middle net worth quintile; ...); Statistics (6 items: Total values; Percentage of total assets or total debts; Number holding asset or debt; Percentage holding asset or debt; ...); Confidence intervals (3 items: Estimate; Lower bound of a 95% confidence interval; Upper bound of a 95% confidence interval).

Estimates (95% CIs) of income level (lowest vs. highest category) and congenital heart defect (CHD) risk.

BackgroundTo investigate the effects of age and sex on the relationship between socioeconomic status (SES) and the prevalence and control status of diabetes mellitus (DM) in Korean adults.MethodsData came from 16,175 adults (6,951 men and 9,227 women) over the age of 30 who participated in the 2008-2010 Korea National Health and Nutrition Examination Survey. SES was measured by household income or education level. The adjusted odds ratios (ORs) and corresponding 95% confidence intervals (95% CI) for the prevalence or control status of diabetes were calculated using multiple logistic regression analyses across household income quartiles and education levels.ResultsThe household income-DM and education level-DM relationships were significant in younger age groups for both men and women. The adjusted ORs and 95% CI for diabetes were 1.51 (0.97, 2.34) and 2.28 (1.29, 4.02) for the lowest vs. highest quartiles of household income and education level, respectively, in women younger than 65 years of age (both P for linear trend < 0.05 with Bonferroni adjustment). The adjusted OR and 95% CI for diabetes was 2.28 (1.53, 3.39) for the lowest vs. highest quartile of household income in men younger than 65 (P for linear trend < 0.05 with Bonferroni adjustment). However, in men and women older than 65, no associations were found between SES and the prevalence of DM. No significant association between SES and the status of glycemic control was detected.ConclusionsWe found age- and sex-specific differences in the relationship of household income and education with the prevalence of DM in Korea. DM preventive care is needed for groups with a low SES, particularly in young or middle-aged populations.

This study aims to examine the prevalence of nasopharyngeal Streptococcus pneumoniae carriage (NSPC) in infants during their first two years of life and to compare the carriage rates among different vaccine groups and country income-levels. This will be achieved through a systematic review of the published literature, specifically focusing on data from cohort studies and randomized controlled trials. A comprehensive search was conducted in four electronic databases: PubMed, Web of Science, ScienceDirect, and Scopus, using a predefined search strategy. Forty-nine articles met the inclusion criteria for this systematic review. According to the results obtained from the random effects model, the pooled mean prevalence of NSPC was 1.68% at birth (95% CI [0.50; 5.47]), 24.38% at 1 to 4 months (95% CI [19.06; 30.62]), 48.38% at 4 to 6 months (95% CI [41.68; 55.13]), 59.14% at 7 to 9 months (95% CI [50.88; 66.91]), 48.41% at 10 to 12 months (95% CI [41.54; 55.35]), 42.00% at 13 to 18 months (95% CI [37.01; 47.16]), and 48.34% at 19 to 24 months (95% CI [38.50; 58.31]). The highest NSPC rates were observed among children aged 4 to 6 months and 7 to 9 months across all vaccine groups. Low-income countries consistently demonstrated the highest NSPC rates across all age categories studied. This systematic review and meta-analysis provide robust evidence of the high prevalence of NSPC in infants aged 4 to 6 months and 7 to 9 months in all vaccine groups, with persistent regional disparities, especially among low-income countries. The study highlights the need for continuous monitoring of NSPC trends, particularly the emergence of non-vaccine serotypes. Policymakers and healthcare providers should leverage these findings to enhance vaccination strategies, aiming to minimize the overall burden of pneumococcal diseases in infants.

AimThe Triglyceride-Glucose (TyG) index, an indicator of insulin resistance, has been proposed as a predictor of cardiovascular diseases. However, its role in predicting stroke risk, particularly in low-income populations, is not well understood. This study aimed to investigate the predictive value of the TyG index for stroke incidence in a low-income Chinese population, with a focus on gender and age-specific differences.MethodsThis 10-year prospective cohort study included 3,534 participants aged ≥45 years from rural areas in northern China. Baseline data on demographic characteristics, lifestyle factors, and clinical measurements were collected. Participants were followed for stroke incidence, categorized into ischemic and hemorrhagic stroke. Multivariate logistic regression models were used to assess the association between the TyG index and stroke incidence, adjusting for potential confounders.ResultsDuring the follow-up period, 368 participants (10.4%) experienced a stroke, with 327 ischemic and 31 hemorrhagic strokes. TyG index was significantly associated with total and ischemic stroke incidence but not hemorrhagic stroke. After adjusting for confounding factors, for every one standard deviation increase in TyG index, the risk of stroke increased by 32% for overall stroke (RR: 1.32; 95% CI: 1.08-1.61; P=0.006) and 39% for ischemic stroke (RR: 1.39; 95% CI: 1.12-1.73; P=0.003). The risk of stroke in the highest TyG tertile levels (tertile 3) increased by 49% (RR: 1.49; 95% CI 1.11-1.99; P=0.007) for overall stroke, compared to those in the lowest tertile levels (tertile 1). For ischemic stroke, the risk of stroke increased by 53% (RR: 1.53; 95% CI 1.12-2.11; P=0.008) in the highest TyG tertile levels (tertile 3) compared to those in the lowest tertile levels (tertile 1).ConclusionThis 10-year prospective cohort study has established the TyG index as an independent predictor of both total and ischemic stroke incidence in a low-income Chinese population. The findings indicate that the TyG index is particularly effective in predicting stroke risk among women and older adults (≥60 years), but not for hemorrhagic stroke. These insights are crucial for improving clinical practice and stroke prevention strategies.