The effect of socioeconomic disadvantage on strategies to end the AIDS epidemic
Bibliographic record
Abstract
As Henry David Thoreau, American author, poet, and philosopher, once stated, “The price of anything is the amount of life you exchange for it”. In the case of life expectancy, differences in the number of additional years above the average that a person of a specific age will live depends on a person's ability to access economic (eg, income), cultural (eg, education), and social (eg, social supports) capital within a society.1Chetty R Stepner M Abraham S et al.The Association Between Income and Life Expectancy in the United States, 2001–2014.JAMA. 2016; 315: 1750-1766Crossref PubMed Scopus (1198) Google Scholar Those with less, relative to their income or social status, often pay the most for the length of life they live. Thus, when assessing life expectancy, the price of anything is everything. In people living with HIV, the uptake of highly active antiretroviral therapy (HAART) has been associated with increased life expectancy. For the first time, people with HIV can expect to live almost as long as their counterparts in the general population.2Samji H Cescon A Hogg RS et al.Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada.PLoS One. 2013; 8: e81355Crossref PubMed Scopus (971) Google Scholar However, as with the general population, several health inequities exist; health outcomes in people living with HIV are affected by ethnic origin, sex, and geography.3Abgrall S Del Amo J Effect of sociodemographic factors on survival of people living with HIV.Curr Opin HIV AIDS. 2016; 11: 501-506Crossref PubMed Scopus (16) Google Scholar The Antiretroviral Cohort Collaboration (ART-CC) noted differences in life expectancy between women and men, individuals with a history of injection drug use and those who do not, and regional differences between North America and Europe, although these differences might be attributed partly to issues of death ascertainment between cohorts.4Antiretroviral Therapy Cohort CollaborationLife expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies.Lancet. 2008; 372: 293-299Summary Full Text Full Text PDF PubMed Scopus (1340) Google Scholar The North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) recorded differences by ethnic origin, with lower life expectancies in African Americans versus other ethnicities.2Samji H Cescon A Hogg RS et al.Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada.PLoS One. 2013; 8: e81355Crossref PubMed Scopus (971) Google Scholar Similarly, the Canadian Observational Cohort Collaboration (CANOC) reported differences between indigenous people and other ethnicities in Canada.5Patterson S Cescon A Samji H et al.Life expectancy of HIV-positive individuals on combination antiretroviral therapy in Canada.BMC Infect Dis. 2015; 15: 274Crossref PubMed Scopus (77) Google Scholar Moreover, results of several studies have shown differences in life expectancy by transmission group, with people who have a history of injection drug use having lower life expectancies than other populations.2Samji H Cescon A Hogg RS et al.Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada.PLoS One. 2013; 8: e81355Crossref PubMed Scopus (971) Google Scholar, 5Patterson S Cescon A Samji H et al.Life expectancy of HIV-positive individuals on combination antiretroviral therapy in Canada.BMC Infect Dis. 2015; 15: 274Crossref PubMed Scopus (77) Google Scholar Finally, the lack of differences in life expectancy between men and women suggests that women living with HIV are more disadvantaged than HIV-negative counterparts in the general population.6Machtinger EL Wilson TC Haberer JE et al.Psychological trauma and PTSD in HIV-positive women: a meta-analysis.AIDS Behav. 2012; 16: 2091-2100Crossref PubMed Scopus (248) Google Scholar In this issue of The Lancet Public Health, Lisa Burch and colleagues7Burch LS Smith CJ Anderson J et al.for the AntiretroviralsSexual Transmission Risk and Attitudes (ASTRA) Study GroupSocio-economic disadvantage and viral load outcomes for HIV positive people on antiretroviral treatment in the UK: cross-sectional and longitudinal analysis.Lancet Public Health. 2016; (published online Oct 12.)http://dx.doi.org/10.1016/S2468-2667(16)30002-0Google Scholar reported that in people treated for HIV in the UK, low socioeconomic status (ie, financial hardship, non-employment, rented or unstable housing status, and no university education) was strongly associated with HAART non-adherence and virological non-suppression. In people who were virally suppressed at baseline, all of the four markers of low socioeconomic status were predictive of subsequent virological rebound. As the researchers note, in this universal health care setting, these findings suggest that the implications of poor socioeconomic status “clearly go beyond inability to pay for treatment and health care, and operate strongly even in people engaged with clinical care”.7Burch LS Smith CJ Anderson J et al.for the AntiretroviralsSexual Transmission Risk and Attitudes (ASTRA) Study GroupSocio-economic disadvantage and viral load outcomes for HIV positive people on antiretroviral treatment in the UK: cross-sectional and longitudinal analysis.Lancet Public Health. 2016; (published online Oct 12.)http://dx.doi.org/10.1016/S2468-2667(16)30002-0Google Scholar In view of these findings, attention should be focused on supporting HAART adherence, an essential component of virological suppression and HIV prevention,8Montaner JS Lima VD Barrios R et al.Association of highly active antiretroviral therapy coverage, population viral load, and yearly new HIV diagnoses in British Columbia, Canada: a population-based study.Lancet. 2010; 376: 532-539Summary Full Text Full Text PDF PubMed Scopus (640) Google Scholar especially in subpopulations that experience increased disadvantage. Factors associated with mortality and unsuppressed viral load resemble those associated with suboptimal adherence. Individuals that show greater vulnerability to suboptimal HAART adherence include individuals who inject drugs and do not access methadone or opioid substitution; have problematic use of crack, cocaine, and alcohol; experience symptoms of depression; and women.9Roux P Carrieri MP Cohen J et al.Retention in opioid substitution treatment: a major predictor of long-term virological success for HIV-infected injection drug users receiving antiretroviral treatment.Clin Infect Dis. 2009; 49: 1433-1440Crossref PubMed Scopus (92) Google Scholar, 10Puskas CM Forrest JI Parashar S et al.Women and vulnerability to HAART non-adherence: a literature review of treatment adherence by gender from 2000 to 2011.Curr HIV/AIDS Rep. 2011; 8: 277-287Crossref PubMed Scopus (102) Google Scholar Marginalisation associated with these factors as well as HIV is compounded by stigma and discrimination, stress, trauma, and lack of social support, all of which undermine HAART adherence.11Katz IT Ryu AE Onuegbu AG et al.Impact of HIV-related stigma on treatment adherence: systematic review and meta-synthesis.J Int AIDS Soc. 2013; 16: 18640-18665Crossref PubMed Google Scholar If the UN 90-90-90 treatment as prevention target is to be achieved even in high and very high human development index countries, the effect of low socioeconomic status on HIV treatment outcomes and ultimately survival needs to be acknowledged. If social disadvantage is not addressed in its many manifestations, health inequities could be exacerbated. This phenomenon is not new; the link between income and survival was prospectively reported early in the epidemic,12Hogg RS Strathdee SA Craib KJ et al.Lower socioeconomic status and shorter survival following HIV infection.Lancet. 1994; 344: 1120-1124Summary PubMed Scopus (86) Google Scholar nor, as noted by Burch and colleagues, is it restricted to HIV.7Burch LS Smith CJ Anderson J et al.for the AntiretroviralsSexual Transmission Risk and Attitudes (ASTRA) Study GroupSocio-economic disadvantage and viral load outcomes for HIV positive people on antiretroviral treatment in the UK: cross-sectional and longitudinal analysis.Lancet Public Health. 2016; (published online Oct 12.)http://dx.doi.org/10.1016/S2468-2667(16)30002-0Google Scholar Clearly, indirect investments targeting socioeconomic status will be needed—and as Thoreau notes, these will not come cheap. JSGM TasP research, paid to his institution, has received support from the BC-Ministry of Health, US NIH (NIDA R01DA036307), UNAIDS, and MAC AIDS Fund. Institutional grants have been provided by Abbvie, Gilead Sciences, J&J, Merck, and ViiV Healthcare. He has served on Advisory Boards for Teva, Gilead Sciences and InnaVirVax. RSH, CP, and SP declare no competing interests. Socioeconomic status and treatment outcomes for individuals with HIV on antiretroviral treatment in the UK: cross-sectional and longitudinal analysesSocioeconomic disadvantage was strongly associated with poorer HIV treatment outcomes in this setting with universal health care. Adherence interventions and increased social support for those most at risk should be considered. Full-Text PDF Open Access
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".