Measuring and decomposing inequity in self-reported morbidity and self-assessed health in Thailand

Yiengprugsawan, Vasoontara; Lim, Lynette; Carmichael, Gordon G.; Sidorenko, Alexandra; Sleigh, Adrian

doi:10.1186/1475-9276-6-23

Cited by 73 publications

(86 citation statements)

References 29 publications

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“…This means that if the marginal effect of a factor is large, but no inequalities exist in the distribution of that factor then its contribution to overall inequality will be low. Conversely, if a factor is concentrated amongst participants differentiated by their SES and the marginal effect of the factor is large then its contribution to the overall concentration index will be large (Yiengprugsawan et al, 2007).…”

Section: Measuring Socio-economic Inequalitymentioning

confidence: 99%

“…The original decomposition method assumes an underlying Ordinary Least Squares (OLS) regression model (Wagstaff et al, 2003); however, decomposition analyses can be undertaken with dichotomous outcomes if based on a linear approximation of the model (O'Donnell et al, 2008;Yiengprugsawan, Lim, Carmichael, Dear, & Sleigh, 2010;Yiengprugsawan, Lim, Carmichael, Sidorenko, & Sleigh, 2007). We used marginal effects derived from a probit model to produce an additive/linear approximation of the model, as expressed by the formula:…”

Section: Measuring Socio-economic Inequalitymentioning

confidence: 99%

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Decomposing socio-economic inequality in colorectal cancer screening uptake in England

Solmi

Wagner

Kobayashi

et al. 2015

Social Science & Medicine

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Colorectal cancer (CRC) is the second largest cause of cancer death in the UK. Since 2010, CRC screening based on Faecal Occult Blood testing has been offered by the NHS in England biennially to all persons age 60 to 69 years. Several studies have demonstrated a gradient in uptake using area-level markers of socio-economic status (SES), but few have examined the individual-level contributors to the gradient. We aimed to quantify the extent of SES inequality in CRC screening uptake in England using individual-level data, and to identify individual factors associated with this inequality.We used data from 1,833 participants (aged 61-69) in Wave 5 (collected in years 2010/11) of the English Longitudinal Study of Ageing (ELSA) eligible for having been sent at least one CRC screening invitation. Uptake was defined by self-report of ever having been screened as part of the National Screening Programme. We assessed socio-economic inequality using the corrected concentration index of uptake against SES rank, which was derived by regressing a range of SES markers against net non-pension household wealth. Other demographic and health-related variables were included in the analysis. Factors associated with inequality were measured using concentration index decomposition.There was a significant pro-rich gradient in screening uptake (concentration index: 0.16, 95% CI:0.11-0.22), mostly explained within our model by differences in non-pension wealth (38.7%), partner screening status (15.9%), sickness/disability (13.5%), and health literacy (8.5%).Interventions aimed at reducing inequalities in CRC screening uptake should focus on improving acceptability of screening in populations with low levels of education and literacy barriers.

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Section: Measuring Socio-economic Inequalitymentioning

confidence: 99%

Section: Measuring Socio-economic Inequalitymentioning

confidence: 99%

Decomposing socio-economic inequality in colorectal cancer screening uptake in England

Solmi

Wagner

Kobayashi

et al. 2015

Social Science & Medicine

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“…Children typically consume less than adults, so counting children as adults may overestimate the living standards of households with children. For developing countries such as Indonesia, empirical studies recommend that a child aged under 15 should be modeled as costing roughly half of what an adult costs (α = 0.5) Yiengprugsawan et al 2007). In addition, household members can share certain types of goods and services, making these consumables cheaper in households of two or more persons than in one-person households.…”

Section: Methodsmentioning

confidence: 99%

Inequity in Dental Care Utilization in the Indonesian Population with a Self-Assessed Need for Dental Treatment

Maharani

2009

Tohoku J. Exp. Med.

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“…Various studies have indicated that incidence of cancer varies across socio-economic position (SEP) groups and health disparities exist between and within countries even within cities (Yiengprugsawan et al, 2007;IAEA, 2011). Social, economical, environmental and political factors have been apparently known as the stem of inequalities in health.…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the role of socioeconomic determinant of health (SDH) helps implement appropriate interventions to reduce disparities. Socioeconomic characteristics such as income, wealth, education, occupation, social class, and insurance coverage are potential indicators for comparison of different groups based on health and social features (Braveman et al, 2001;Yiengprugsawan et al, 2007;Natale-Pereira et al, 2011). The proportion of cancer incidence across SEP is dissimilar by cancer types; while some cancers are more common in higher socioeconomic positions such as prostate and breast cancers, a variety of cancers prevail in lower SEP groups such as lung and cervical cancers (Yin et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Correlation of Cancer Incidence with Diet, Smoking and Socio-Economic Position Across 22 Districts of Tehran in 2008

Rohani-Rasaf

Abdollahi²,

Jazayeri³

et al. 2013

Asian Pacific Journal of Cancer Prevention

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Background: Variation in cancer incidence in geographical locations is due to different lifestyles and risk factors. Diet and socio-economic position (SEP) have been identified as important for the etiology of cancer but patterns are changing and inconsistent. The aim of this study was to investigate correlations of the incidence of common cancers with food groups, total energy, smoking, and SEP. Materials and Methods: In an ecological study, disaggregated cancer data through the National Cancer Registry in Iran (2008) and dietary intake, smoking habits and SEP obtained through a population based survey within the Urban Health Equity Assessment (Urban-HEART) project were correlated across 22 districts of Tehran. Results: Consumption of fruit, meat and dairy products adjusted for energy were positively correlated with bladder, colorectal, prostate and breast and total cancers in men and women, while these cancers were adversely correlated with bread and fat intake. Also prostate, breast, colorectal, bladder and ovarian cancers had a positive correlation with SEP; there was no correlation between SEP and skin cancer in both genders and stomach cancer in men. Conclusions: The incidence of cancer was higher in some regions of Tehran which appeared to be mainly determined by SEP rather than dietary intake. Further individual data are required to investigate reasons of cancer clustering.

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Measuring and decomposing inequity in self-reported morbidity and self-assessed health in Thailand

Cited by 73 publications

References 29 publications

Decomposing socio-economic inequality in colorectal cancer screening uptake in England

Decomposing socio-economic inequality in colorectal cancer screening uptake in England

Inequity in Dental Care Utilization in the Indonesian Population with a Self-Assessed Need for Dental Treatment

Correlation of Cancer Incidence with Diet, Smoking and Socio-Economic Position Across 22 Districts of Tehran in 2008

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