Socioeconomic inequalities in the diffusion of health technology: Uptake of coronary procedures as an example
Australian Centre for Economic Research on Health, The Australian National University, Australia. Social Science [?] Medicine
(Impact Factor: 2.89).
01/2011; 72(2):224-9. DOI: 10.1016/j.socscimed.2010.11.002
This paper examines socioeconomic lags in the diffusion of high technology health care, focusing on the diffusion of coronary procedures in people with ischaemic heart disease. Using linked hospital and mortality data, we studied patients admitted to Western Australian hospitals with a first admission for acute myocardial infarction between 1989 and 2003 (n = 27,209). An outcome event was the receipt, within a year, of a coronary procedure-angiography, angioplasty and/or coronary artery bypass surgery (CABG). Socioeconomic status (SES) was assigned to each individual using the SEIFA Index of Disadvantage. Cox regression was used to model the association between SES and procedure rates in five consecutive three-year time periods. Angiography and CABG showed socioeconomic lags in diffusion, with rates peaking earlier in higher SES patients, such that the inequality patterns were consistent with the inverse equity hypothesis. The evidence for a lag in diffusion for angioplasty was weaker. Overall, that there is some evidence for a lag in diffusion of health technology indicates that it is essential to consider trends over time when examining the equity impact of health technologies.
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ABSTRACT: HIV prevalence trends in Tanzania differ between socioeconomic groups. While HIV prevalence was initially higher among those with higher levels of educational attainment, it has fallen fastest among these groups. Among those with lower levels of education HIV prevalence has been stable. The behavioural dynamics underlying this phenomenon remain unclear, and a theory to guide interpretation of these trends and enable predictions of future patterns has not emerged.
We analysed data from two large nationally representative surveys conducted in Tanzania in 2003/2004 and 2007/2008. We focused on young people aged 15 to 24 years and explored reports of (i) first sex, (ii) having had more than one sexual partner in the last year and (iii) unprotected last sex with a non-cohabiting partner. Our analysis explored whether the behaviours differed by educational attainment in 2003/2004 and in 2007/2008, and whether changes over time in these behaviours differed between educational groups.
The rate of first sex was lower among more educated males in 2007/2008 but not in 2003/2004, and among females in both surveys. The change over time in educational patterning of the rate of first sex in males was mostly due to a declining rate among the secondary educated groups. Among males, having had more than one sexual partner in the last year was associated with lower education in 2003/2004 and in 2007/2008. Among females, those with less education were more likely to report more than one partner in 2003/2004, although by 2007/2008 there was little association between education and reporting more than one partner. Unprotected last sex with a non-cohabiting partner was less common among the more educated. Among both sexes this decreased over time among those with no education and increased among those with secondary education.
Patterns of behaviour suggest that differences in HIV incidence might explain trends in HIV prevalence among different educational groups in Tanzania between 2003/2004 and 2007/2008. The "inverse equity hypothesis" from child health research might partially help explain the changing social epidemiology of HIV incidence in Tanzania.
Journal of the International AIDS Society 06/2012; 15 Suppl 1(Suppl 1):1-7. DOI:10.7448/IAS.15.3.17363 · 5.09 Impact Factor
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To present details of the Norwegian Family Based Life Course Study.
All Norwegians participating in censuses from 1960 to 2001 were included. In addition to the personal identity number, we used household and family information from the 1960 census to link family members together. The NFLC study is further linked to other health registers and surveys.
The proportion included and alive in 1960 increased from 67 % among those born in 1900 to more than 90 % for those born after 1940. In all, 5,266,270 were included. This combined family linkage approach gave 85 % parental linkage for those born in 1940 that dropped to 20 % of those born in 1930. The proportion with misclassified parents was less than 0.5 %. In all, 3,564,582 individuals were linked to their parents.
The NFLC is one of the largest follow-up of individuals over several decades in their life course. The comprehensive multigenerational, family linkage within the database contributes to large-scale use of various designs for investigating life course determinants.
International Journal of Public Health 06/2012; 58(1). DOI:10.1007/s00038-012-0379-4 · 2.70 Impact Factor
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To estimate the impact family factors shared by siblings has on the association between length of education and cause-specific mortality in adulthood.
The study population (n = 871 367) was all Norwegians born 1940-59 having one or more sibling within the cohort and alive in 1990. Length of education was obtained in 1990. Follow-up of deaths was from 1991 to 2008 when participants were aged 32-68 years.
Sixty-five per cent of participants had one or more siblings who had completed a different number of years of formal education. A one-category difference in education was associated with a 30% increase in the hazard rate of death by all causes among men in the cohort analysis and 23% in within siblings analysis, and in women, increases were 22% and 14%, respectively. For cardiovascular disease, increases were 36% and 25% in men and 51% and 36% in women. For lung cancer, they were 48% and 29% in men and 53% and 22% in women. External causes and alcohol-related causes in men were generally similar in both analyses.
This study suggests that at least some of the educational inequalities in all-cause, cardiovascular disease and lung cancer, external and alcohol-related mortality are explained by factors shared by siblings.
International Journal of Epidemiology 10/2012; 41(6). DOI:10.1093/ije/dys143 · 9.18 Impact Factor
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