3 rd International Workshop on Women and HIV Toronto, January 14 th, 2013
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1 Anita C. Benoit 1, Lucia Light 2, Ann N Burchell 2, Sandra Gardner 2,3, Shari Margolese 1, Wangari Tharao 4, Gladys Kwaramba 5, Angela Kaida 6, Alexandra de Pokomandy 7, Bob Hogg 8, Mona Loutfy 1,4, CHIWOS Team, OHTN Cohort Study Team Women s College Research Institute 1, Ontario HIV Treatment Network 2, University of Toronto 3, Women s Health in Women s Hands CHC 4, McMaster University 5, Simon Fraser University 6, McGill University Health Centre, McGill University 7, British Columbia Centre for Excellence in HIV/AIDS 8 3 rd International Workshop on Women and HIV Toronto, January 14 th,
2 The traditional territory of the Mississauga of the New Credit OCS Study Team Sean B Rourke (PI) Ann N Burchell (co-pi) Ahmed M Bayoumi Anita Rachlis Curtis Cooper Don Kilby Edward Ralph Fred Crouzat Irving E Salit Janet Raboud Jeffrey Cohen John Cairney Kevin Gough Marek Smieja Mona Loutfy Nicole Mittmann Roger Sandre Sandra Gardner Sergio Rueda Wendy Wobeser Ontario CHIWOS Study Team Mona Loutfy (PI) Ann N Burchell Cindy-Lou Brisebois Gladys Kwaramba Haoua Inoua Johanna Lewis Saara Greene Stephanie Smith Wangari Tharao OCS/OHTN Staff Kevin Challacombe Stephanie Choi Mark Fisher Robert Hudder Lucia Light Michael Manno Veronika Moravan Nahid Quereshi Samantha Robinson OCS Governance Committee Tony Di Pede (Chair) Adrian Betts Evan Collins Tracey Conway Patrick Cupido Brian Finch Michael Hamilton Clemon George Troy Grennan Claire Kendall Rick Kennedy Ken King John MacTavish Carol Major Shari Margolese Colleen Price Lori Stoltz Data Linkage Public Health Laboratories, Ontario Agency for Health Protection and Promotion Funding Anita C. Benoit is supported by the CTN James Kreppner Postdoctoral Fellowship Award We thank all interviewers, data collectors, research associates and coordinators, nurses and physicians who provide support for data collection and extraction 2
3 Has not been clearly articulated and defined 1. Ridner SH..J Adv Nurs (5): Drapeau A et al. Epidemiology of psychological distress, mental illnesses Understanding, prediction and control Emotional suffering characterized by symptoms of depression (e.g., lost interest; sadness; hopelessness) and anxiety (e.g., restlessness; feeling tense) 1 and sometimes somatic symptoms (e.g., insomnia; headaches; lack of energy) that may vary across cultures 2,3. 1. Mirowsky and Ross. J Health Soc Beh : Kleinman A. Rethinking psychiatry. From cultural category to personal experience. New York: The Free Press Kirmayer LJ. Soc Scien Med : Review in Drapeau et al. Epidemiology of psychological distress, mental illnesses Understanding, prediction and control
4 Risk factors and protective factors for psychological distress 1. Socio-demographic factors 2. Stress-related factors 3. Personal resources Prevalence of psychological distress generally higher in women than men in all age groups and in most countries (Rev. Drapeau, 2012). 4
5 1. Socio-demographic factors Number of children, denied housing, disability Ethnic stigmatisation loss of self esteem and lower access to social resources (housing, income, employment) 2. Stress-related factors Life transition, marital breakdown, family and work-related conflict Chronic health problems 3. Personal resources Inner resources - Loss of self-esteem and control over one s life External resources - Lack of social networks and social support 5
6 To identify socio-demographic and clinical correlates of psychological distress among women accessing HIV care in Ontario. 6
7 OHTN Cohort Study (OCS) Anonymous, prospective observational, open dynamic cohort study Data collection from chart extractions, linkages with PHL databases, and structured interviews Eligibility criteria: 1) > 16 years of age, 2) consent, 3) access care and reside in Ontario, and 4) Positive HIV antibody test or other laboratory evidence of HIV infection Eligibility for psychological distress analysis Self-identify as a woman (n=337) Complete Kessler psychological distress scale (K10) OCS core questionnaire between
8 As measured by the Kessler Psychological Distress Scale (K10) Quantify the frequency and severity of anxiety- and depression-related symptoms. Ten questions with 5-level response option: All of the time (5), most of the time (4), some of the time (3), a little of the time (2), none of the time (1) Score range (10-50): Low 10-19/moderate 20-24/high / very high Assist in planning and monitoring treatment/interventions for anxiety and depression for women living with HIV. Data analysis Logistic regression analysis using K10 scores with 19 as cutoff and age and ethnicity were a priori exposures of interest Compared proportion experiencing psychological distress using Chi-square test 8
9 Median (IQR) Age at interview 43 (35-50) Years living with HIV 11 (6-16) Total K10 scores 18 (13-25) Psychological distress (n=337) Moderate to very high 143 (42.4%) (20-50) Very high (30-50) 51 (15.1%) High (25-29) 35 (10.4%) Moderate (20-24) 57 (16.9%) Low (10-19) 194 (57.6%) *p<0.05 Age (2 cat, n=337) < % > % Ethnicity/race (n=337) Aboriginal 16.9% Black/African 28.8% Multiple race 8.0% White 46.3% Sexual orientation Bisexual 1.49% Gay 1.19% Heterosexual 94.9% Lesbian 1.49% Other 0.9% Employment * (n=335) Immigration (n=322) Canadian-born 63.7% Canadian citizen 19.9% Landed/Permanent resident Disability 49.9% Student/retired/Employed 42.1% Unemployed 8.06% Education * (n=336) completed College or more 37.5% Some college or less 62.5% Gross Income * (n=305) $20K-$80K 38.7% >$80K 10.5% <$20K 55.8% 12.7% Other 3.73% 9
10 Other Immigration status Landed/permanent resident Canadian citizen Canadian-born Other Sexual orientation Lesbian Heterosexual Gay Bisexual Ethnicity/race White Multiple race Black/African Aboriginal Age >40 < Proportion of moderate to very high psychological distress (%) 10
11 <$20K Gross income * >$80K $20K-$80K Education * Completed some college or less Completed college or more Employment * Unemployed Student/retired/employed Disability * p< Proportion of moderate to very high psychological distress (%) 11
12 Clinical characteristics Hepatitis C (n=337) Yes 26.1% No 73.9% Viral load (n=337) Undetectable (<50) 74.2% Suppressed (50-200) 6.53% Not suppressed (>200) 19.3% CD4 cell count * (n=337) >200 cells/mm % <200 cells/mm 3 9.5% Years living with HIV (n=335) < % 5< years < % 1< years < % > % Socio-demographic characteristics Hazardous alcohol use * (n=334) Yes 26.1% No 73.9% Country high HIV prevalence * (n=337) Yes 30.9% No 69.1% IDU last 6 months * (n=337) Yes 20.8% No 79.2% Live with their child(ren) * (n=337) True 40.9% False 59.1% Live alone * (n=337) Yes 25.8% No 74.2% Years living in Canada * (n=306) Born in Canada 67.0% < % > % 12
13 Hazardous alcohol use * Country high HIV prevalence * IDU last 6 months * Live with their child(ren) * Live alone * Yes No Yes No Yes No True False Yes No Years living in Canada * Born in Canada < 5 > 5 *p< Proportion of moderate to very high psychological distress levels (%) 13
14 Hepatitis C Viral load Yes No Undetectable (<50) Suppressed (50-200) Not suppressed (>200) CD4 T cell count * > 200 < 200 Years living with HIV <2 5 <years< 10 1 <years <5 >10 * p< Proportion of moderate to very high psychological distress levels (%) 14
15 AOR 95% CI Education Some college or less vs. Completed college or more Living in a household Without their children vs. with their children Employment Disability vs. Student/retired/employed/unemployed CD4 cell count <200 cells/mm 3 vs. >200 cells/mm Other covariates were age (<40 vs. >40) and race (Aboriginal, Black/African, multiple race each vs. White), not significant. *Adjusted Odds Ratio - AOR 15
16 Strongest correlates for high psychological distress among women in HIV care in Ontario: Being on disability CD4 counts below 200 cells/mm 3 Living in a household without their children Education Age and ethnicity were not associated with psychological distress. 16
17 Structural systems shaping the demography of women living with HIV can result in high levels of psychological distress. May act as barriers to accessing psychological health care services. Are women accessing HIV care engaged in care? Disability Economic constraints, stigma, unable to work Living in a household without their child(ren) Immigration, child welfare, divorce or loss of custody Targets for interventions: socio-demographic factors 17
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