Barriers to health care services for migrants living with HIV in Spain

Barriers to health care services for migrants living with HIV in Spain Abstract Background In Spain, migrants are disproportionately affected by HIV and experience high rates of late diagnosis. We investigated barriers to health care access among migrants living with HIV (MLWH) in Spain. Methods Cross sectional electronic survey of 765 adult HIV-positive migrants recruited within 18 health care settings between July 2013 and July 2015. We collected epidemiological, demographic, behavioral and clinical data. We estimated the prevalence and risk factors of self-reported barriers to health care using multivariable logistic regression. Results Of those surveyed, 672 (88%) had information on health care access barriers: 23% were women, 63% from Latin America and Caribbean, 14% from Sub-Saharan Africa and 15% had an irregular immigration status. Men were more likely to report barriers than women (24% vs. 14%, P = 0.009). The main barriers were: lengthy waiting times for an appointment (9%) or in the clinic (7%) and lack of a health card (7%). Having an irregular immigration status was a risk factor for experiencing barriers for both men (OR: (4.0 [95%CI: 2.2–7.2]) and women (OR: 10.5 [95%CI: 3.1–34.8]). Men who experienced racial stigma (OR: 3.1 [95%CI: 1.9–5.1]) or food insecurity (OR: 2.1 [95%CI: 1.2–3.4]) were more likely to report barriers. Women who delayed treatment due to medication costs (6.3 [95%CI: 1.3–30.8]) or had a university degree (OR: 5.8 [95%CI: 1.3–25.1]) were more likely to report barriers. Conclusion Health care barriers were present in one in five5 MLWH, were more common in men and were associated to legal entitlement to access care, perceived stigma and financial constraints. Introduction Migrants accounted for 37% of the 29 747 new HIV diagnoses reported in the European Union/European Economic Area (EU/EEA) by the European Centre for Disease prevention and Control (ECDC) in 2015; and most of them (13%) were from Sub-Saharan Africa (SSA).1 The disproportionate burden of HIV infection experienced by migrants in Europe also shapes the Spanish HIV epidemic: 30% (n = 1037) of the 3428 new diagnoses reported in 2015 by the Spanish Surveillance Information System on new HIV diagnosis (SINIVIH) occurred in migrants, 237 (23%) of which were migrant women.2 Furthermore, in Spain people from Latin America and the Caribbean (LAC) not only account for the majority of the migrant population (37%)3 but are also the most relevant group among newly diagnosed HIV+ migrants (53%).2 Late HIV diagnosis is more frequent among migrants than among Spaniards,2 and this not only has a negative impact on the patient′s clinical outcomes but can also lead to increased HIV transmission rates within the community.4,,5 Several studies have shown how late HIV diagnosis among migrants is related to the presence of barriers to access of health promotion and health care services, including HIV testing, treatment and prevention.6 Migrants may be differently affected by health care barriers depending on their demographic profile (gender, country of origin, sexual orientation, etc.)2,,7 and on the socioeconomic vulnerabilities they experience.8 In Spain, these barriers might be larger for men and for migrants from non-Spanish speaking countries. In a Spanish study, high rates of health care barriers resulting in delayed HIV diagnosis occurred among SSA migrants7 while among people from LAC health care access challenges were reflected in an increased use of emergency services compared to native Spaniards.9 Cultural differences, poverty, uncertainty about health entitlements, lack of understanding of the health care system and fear of discrimination can all contribute to health care barriers.10 These barriers can occur at the patient level (sex, age, education, income, immigration status, personal health practices, etc.), the health care provider level (cultural sensitivity and competence, communication style, bilingualism, etc.) and the health system level (health policy, referrals, opening hours, waiting times, etc.).10,,11 Gender differences often influence health care access, especially among migrants: male migrants are often seeking care only in case of emergency,12 while female migrants are more likely to seek care via antenatal and child services.13 Research examining barriers to health care services among HIV-positive migrants population in Spain is scarce.6,,11 The current study aims to describe the nature and the prevalence of self-perceived barriers to access health care for migrants living with HIV (MLWH) in Spain, and assess risk factors associated with these barriers, while exploring gender differences. Methods aMASE The advancing Migrant Access to health Services in Europe (aMASE) study is a research project conducted within the European Network of Excellence for HIV cohort studies (EuroCoord). aMASE took place in 57 HIV clinics and hospitals across nine EU countries and its methodological design has been previously described.14 This paper focuses on the Spanish results of the study (aMASE-Spain). aMASE-Spain A cross-sectional electronic survey was conducted in 18 HIV clinics and hospitals across 7 Spanish regions, between July 2013 and July 2015. Participating sites are listed in the Annex (Annex 1). The research staff in each site identified and recruited eligible subjects among their HIV-positive patients who fulfilled the following criteria: (1) diagnosed within 5 years of the study date, (2) aged ≥18 years, (3) foreign-born, (4) resident in Spain for a minimum of 6 months and (5) able to complete, either alone or with assistance, a computer-assisted self-interview (CASI) in any of the 15 available languages (Amharic, Arabic, Dutch, English, French, German, Greek, Italian, Polish, Portuguese, Russian, Turkish, Tigrinya, Spanish and Somali). Sample size An overall effective sample size of 1600 individuals was pre-determined at the EU-level for the entire aMASE study.14 In Spain, 765 participants were recruited, which exceeded the minimum sample size required (N = 100) for the country. Using post-hoc analyses, we were able to determine that this sample size would provide a precision within 3.7% for our main estimate of interest. Sampling strategy Interested sites with the ability to recruit at least 40 migrants for the study, and with sufficient financial and human resources to conduct the survey, were invited to participate. A convenience sampling strategy was used to recruit study participants: eligible patients attending participating clinics were approached by research team members and invited to participate in the survey. Data collection We used two questionnaires: a self-administrated Patient Questionnaire (PTQ) (with socio-economic, epidemiological, clinical, behavioral, migratory and health services use data); and a Clinical Data Questionnaire (CDQ) completed by clinicians (CD4 count, viral load, HIV virus subtype). Both questionnaires were linked using an anonymous patient study number. Statistical analysis The primary outcome variable was defined by the presence of at least one self-reported barrier to health care services (‘Barriers in access to health care services’). Explanatory variables included sociodemographic and clinical characteristics such as: age, geographic origin, education, employment, income, HIV transmission route, immigration status (irregular immigrants were defined as: asylum seekers, refugees and undocumented migrants), late diagnosis (CD4 count <350 cells/mm3 at the time of diagnosis), perceived HIV stigma, perceived race stigma, perceived sexuality stigma, late diagnosis, AIDS at presentation, difficulty accessing food and delayed medical treatment due to medication costs. Separate analyses were carried out for men and women, since gender was conceptualized as an effect modifier for access to care.13 We calculated frequencies for categorical variables and median and interquartile range (IQR) for continuous ones. Bivariate associations between exposure variables and the variable of interest were calculated. Risk factors for health care barriers were identified through logistic regression. Variables with a P values lower than 0.10 in bivariate analysis were used to fit multivariable models for both men and women. We constructed the final model through a forward procedure and the best model was selected comparing maximum likelihood values. We calculated odds ratios (ORs) with their 95% confidence intervals (95% CI) for each variable included in the final model. Statistical significance was evaluated using the Wald test and all analyses were performed using STATA software (Version 14.0, College Station, Texas). Ethical committee clearance The study received approval from the Ethics Committee of Carlos III Spanish Health Institute and from some of the ethics boards of the hospitals/clinics conducting the study. All participants consented to take part in the study. Results Sociodemographic and clinical characteristics In total, 775 individuals were interviewed. We excluded 93 (12%) individuals who had no information on the primary outcome of interest. We also excluded 10 (1%) transgender individuals because their number was too small to include in a stratified analysis and draw meaningful conclusions. Table 1 summarizes the sociodemographic characteristics of the 672 participants included in the analysis. Among those, 154 (23%) were female and 410 (61%) self-identified as men who have sex with men (MSM). Among women, 50% were from LAC and 31% from SSA, while among men, 66% were from LAC and 11% from Western Europe. Non-regular migrants constituted 15% of the sample; this proportion was higher among women (21%) and people from SSA (33%). Migrants from SSA were more likely to be diagnosed late (65%) and among LAC men, 74% identified themselves as MSM (data not shown). Table 1 Descriptive characteristics of the study participants by sex Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Table 1 Descriptive characteristics of the study participants by sex Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Self-perceived barriers to health care access One in five participants reported experiencing at least one barrier to accessing health care in Spain (22%) (table 2). Men (24%) were more likely to report barriers than women (14%).The top three self-perceived barriers to accessing health care were the same in males and females: waiting time for an appointment, lacking a health card and waiting times in clinic. Barriers were more common among migrants from SSA (27%) and in those from other geographical origins (North Africa, North America, Asia and Oceania) (36%), compared to the rest of the participants, notably Spanish speaking migrants. Migrants from these first two groups, mostly experienced barriers related to language, waiting time in the clinic and difficulty finding a general practitioner (GP) (data not shown). Finally, rate of self-perceived barriers to health care was highest among persons who inject drugs (PWID) (38%) compared to heterosexuals (HTX) (18%) and MSM (24%). Migrants who inject drugs were more likely to perceive waiting time for appointment (14%) and uncertainty towards health entitlements (24%) as barriers to health care access (data not shown). Table 2 Prevalence of self-perceived barriers to health care access and type of barriers experienced by sex Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 * At least one barrier to access health care. Table 2 Prevalence of self-perceived barriers to health care access and type of barriers experienced by sex Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 * At least one barrier to access health care. Determinants of barriers to health care access Univariate analysis determinants of barriers to health care access for men and women are presented in table 3. Among women, health care access barriers were more likely to be perceived in those with university education, an irregular immigration status and medication delay due to treatment cost. Among men, barriers were associated with being from SSA, having a primary or university education, not earning their own wage, having an irregular immigration status, experiencing difficulties accessing food, delaying medical treatment due to medication costs and being stigmatized for their race, sexuality or HIV-positive status. Overall 14% of women and 25% of men who reported barriers to health care were diagnosed late for HIV (CD4 count at diagnosis <350 cells/mm3). Table 3 Prevalence and risk factors of barriers to access health care services by sex (Univariable analysis) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Table 3 Prevalence and risk factors of barriers to access health care services by sex (Univariable analysis) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) The multivariable logistic analysis (table 4) shows the adjusted risk factors associated with barriers to health care access. For women, these risk factors included: having an irregular immigration status; having a university degree and delaying non-HIV medical treatment due to medication costs. Among men we identified the following risk factors: irregular immigration status, difficulty accessing food and experiencing racial stigma. Table 4 Factors associated with barriers in access to health care services among women and men (adjusted model) Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Table 4 Factors associated with barriers in access to health care services among women and men (adjusted model) Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Discussion In this study, we found that approximately one in seven migrant women and one in four migrant men living with HIV in Spain, and already linked to HIV care, reported having previously experienced at least one barrier to access health care. The nature, the prevalence and the risk factors of these barriers are different among men and women and reflect the extreme social and economic vulnerability experienced by a relevant proportion of HIV positive migrants living in Spain. We found that HIV positive migrant men living in Spain were more likely to report health care barriers compared to women. This finding is in accordance with results from a study conducted in the United Kingdom by Burns et al.8 The limited engagement of men in health care may reflect their propensity to perceive physicians as emergency care providers rather than continuous care providers, thus only seeking help for complex health issues,12,,15 On the other hand, women’s entry into the health care system is facilitated by their role as caregiver accompanying relatives or through reproductive and child health care services.16 Despite beneficiating from this ‘front door’ into the health care system, women often face other types of barriers that hinder their access to care. For instance, a study by Guionnet et al. reported that HIV-positive migrant women living in Spain often perceived their condition as lethal and financially unaffordable17 even though HIV combination antiretroviral therapy (cART) is free for all people living with HIV residing in Spain, regardless of their legal status.18 This could either reflect migrant women’s limited knowledge of their health care entitlements or them prioritizing their work and providing for their families over attending clinic visits during working hours.19,,20 Several structural barriers to care were identified in this study. Extended waiting times for appointment and during clinic visits were the most commonly reported barriers for both male and female migrants. Although the sample size for women was small, these results support previous work that has been done in this area21 and suggest that the rigid schedules of health care centers do not fit migrants’ demanding work conditions and timetables. This was corroborated in studies showing that migrants perceive occupational strains linked to job insecurity and inflexible work shifts as a major impediment to their health and are often unable to access care during work hours due to fear of losing their employment.20,,22 In our study, we also found that migrants from SSA were more likely to experience barriers, especially language barriers, which could eventually exacerbate their social exclusion and lead to their inability to understand the health care system and their health care rights entitlements.23 On the other hand, migrants from LAC, who are native Spanish speakers, did not report any language barrier. We identified gender specific determinants of health care barriers. For women, education and financial considerations were specific risk factors for barriers. We found that women with a university degree were more likely to experience barriers to health care and were also less likely to have children, which may cause them to under use antenatal services.24 Highly educated migrant Ecuadorian women who had to accept jobs for which they are over qualified have been reported to experience poor mental health outcomes,25 which combined with financial strains and occupational dissatisfaction may exacerbate pre-existing health care barriers. Women who delayed non-HIV treatments due to financial restrictions were also more likely to report health care barriers, thus highlighting a subgroup of women who are particularly vulnerable as they generally earn less income and have a lower rate of employment than men.26,,27 Men-specific risk factors included difficulty accessing food and racial stigma. Limited food access has been previously associated with low health prioritization and an increased need to focus on food and housing procurement.28 Furthermore, in those living with HIV, food insecurity is also a risk factor for treatment interruption, morbidity and mortality.29,,30 As for racial stigma, this is a well-documented risk factor for disparities in health access.31,,32 In people at risk of or living with HIV, perceived racial stigma has been associated with health care barriers such as: lower trust in health care provider, higher likelihood of missing medical appointments, fear of HIV testing and lower adherence to cART.33,,34 In both migrant men and women, having an irregular immigration status was the main risk factor for experiencing barriers. In 2012, Spain revoked free and universal access to the health care system for undocumented migrants, with some exceptions such as for conditions of public health relevance which include HIV infections.35,,36 Although these rights were partly returned in late 2015, irregular migrants are still denied access to the health card.18 National policies restricting health care access for undocumented migrants have an important negative impact on migrant health as has been widely acknowledged by ECDC and WHO.22,,37 HIV-positive migrants are particularly vulnerable due to HIV social stigma and the chronic nature of the infection, which requires life-long treatment and care.38,,39 Governments should guarantee free access to health care system regardless of migrants’ legal status in order to ensure the timely access to treatment, thus resulting in better clinical outcomes for the patient and decreased HIV transmission at community level.39 Restricting health care access for undocumented migrants not only hampers public health measures but also goes against the fundamental principles of equity in health care access outlined in the international human rights laws which have been ratified by all EU/EEA countries.39 Facilitating health care access to undocumented immigrants is therefore a human rights obligation. We would like to acknowledge some study limitations. First, the recruitment of our sample from HIV health facilities has led to an underrepresentation of the most vulnerable patients who still have not accessed the health care system, or are still unaware of their HIV status. It is expected that barriers would be more extreme in this group. Second, there may have been an information bias due to the sensitivity of the information collected (e.g. legal status, sexual orientation, and so on), which we minimized through the migrant-friendly nature of the recruiting centres and by privacy provided by self-reporting to electronic questionnaires. Thirdly, we could not compare the prevalence or effect of barriers between immigrants and native Spaniards, due to the lack of a control group. Finally, due to insufficient sample size, our analysis among women is underpowered, which may lead to imprecision in our estimates within this group. Sample size restriction also made it difficult to assess the risk factors for health care barriers according to specific geographical origins. In spite of this limitation, we have chosen to stratify findings by sex given that findings show clear gender differences that would otherwise become invisible. Conclusions The current findings indicate that barriers to health care are common among HIV-positive migrants living in Spain linked to care. Defining and understanding these barriers are therefore key to the development of policies and effective care programs tailored to reach HIV-positive migrants. New health strategies should aim to reduce structural barriers to health care access, especially for vulnerable groups such as undocumented migrants. This is also crucial for achieving UNAIDS 90–90–90 targets with the ultimate goal to control the HIV epidemic by 2020.40 Acknowledgements This research would not have been possible without the collaboration of the patients, physicians, nurses, data managers and researchers who have taken part in this project. The aMASE Research Group Anne-Françoise Gennotte, Cécile La Morté Barthélemy, Claudia Wengenroth, Giota Touloumi, Gountas Ilias, Koulai Loumpiana, Anagnostou Olga, Gundolf Schüttfort, Claudia Wengenroth, Henrique Barros, Paula Meireles, Julia del Amo, Débora Álvarez, Susana Monge, Bruno Ledergerber, Cornelia Staehelin, Maria Prins, Janneke Bill, Freke Zuure, Tullio Prestileo, Nicolla Petrosillo, Fiona Burns, Ibi Fakoya, Jorge del Romero J, Carmen Rodríguez, Mar Vera, Isabel Río, Vanessa Paredes, Nieves Sanz, Fernando Dronda, María Velasco Arribas, María Gracia Mateo, Mar Gutiérrez, Pere Domingo, Hernando Knobel, Teresa Pellicer, Emma Fernández, María del Carmen Ligero, Margarita Robau, José María Miró, Inmaculada Ocaña, Joaquín Burgos, María José Barberà, Maider Arando, Félix Gutiérrez, Mar Masía, José Ramón Blanco, Josefina Belda-Ibáñez, Elisa Fernández García, Trinidad Zafra Espinosa, Ana López-Lirola, Miguel Salavert, Marta Montero, Eva Calabuig, Sandra Cuellar, Pompeyo Vinciana, Reyes Palacios, Federico García, Alejandro Peña, José Antonio Iribarren, Mirian Aguado, Federico Pulido, Ángel Portillo and José Luis Gómez-Sirvent. Funding This work is part of aMASE (Advancing Migrant Access to Health Services in Europe) which was funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under EuroCoord grant agreement n° 260694. This work has been supported by the Spanish Medical Fund Research (PI12/02134) and Spanish Network of Excellence on HIV (RD12/0017/0018, RD16CIII/0002/0006), FIPSE (Fundación de Investigación y Prevención del SIDA) and CIBERESP. This research would not have been possible without the collaboration of the patients, physicians, nurses, data managers and researchers who have taken part in this project. Conflicts of interest: None declared. Key points 1 in 5 HIV-positive migrant living in Spain and linked to care experience barriers to healthcare access. HIV-positive male migrants are more likely to report barriers than their female counterpart. Having an irregular immigration status was the main risk factor for experiencing barriers in both male and female HIV-positive migrants. New health policies and strategies should aim to reduce structural barriers to healthcare access in HIV-positive migrants. References 1 European Centre for Disease prevention and Control . HIV/AIDS surveillance in Europe 2015, 2015 . Available at: http://www.euro.who.int/__data/assets/pdf_file/0019/324370/HIV-AIDS-surveillance-Europe-2015.pdf? ua=1 (1 January 2017, date last accessed). 2 Área de Vigilancia de VIH y Comportamientos de Riesgo . Vigilancia Epidemiológica del VIH y sida en Espãa: Sistema de Información sobre Nuevos Diagnósticos de VIH y Registro Nacional de Casos de Sida. Plan Nacional Sobre el SIDA, 2016 . Available at: https://www.msssi.gob.es/ciudadanos/enfLesiones/enfTransmisibles/sida/vigilancia/InformeVIH_SIDA_2016.pdf (1 January 2017, date last accessed). 3 Instituto Nacional de Estadística . Cifras de población residente en España, 2017 .Available at: http://www.ine.es/ (1 January 2017, date last accessed). 4 Cohen MS , Chen YQ , McCauley M , et al. Prevention of HIV-1 infection with early antiretroviral therapy . N Eng J Med 2011 ; 365 : 493 – 505 . Google Scholar CrossRef Search ADS 5 Mocroft A , Lundgren JD , Sabin ML , et al. Risk factors and outcomes for late presentation for HIV-positive persons in Europe: results from the Collaboration of Observational HIV Epidemiological Research Europe Study (COHERE) . PLoS Med 2013 ; 10 : e1001510 . Google Scholar CrossRef Search ADS PubMed 6 Alvarez-del Arco D , Monge S , Azcoaga A , et al. HIV testing and counselling for migrant populations living in high-income countries: a systematic review . Eur J Pub Health 2013 ; 23 : 1039 – 45 . Google Scholar CrossRef Search ADS 7 Monge S , Alejos B , Dronda F , et al. Inequalities in HIV disease management and progression in migrants from Latin America and sub-Saharan Africa living in Spain . HIV Med 2013 ; 14 : 273 – 83 . Google Scholar CrossRef Search ADS PubMed 8 Burns FM , Imrie JY , Nazroo J , et al. Why the(y) wait? Key informant understandings of factors contributing to late presentation and poor utilization of HIV health and social care services by African migrants in Britain . AIDS Care 2007 ; 19 : 102 – 8 . Google Scholar CrossRef Search ADS PubMed 9 Muñoz-de Bustillo R , Anton Perez JI . Use of public health services by Latin American immigrants in Spain . Salud publica de Mexico 2010 ; 52 : 357 – 63 . Google Scholar CrossRef Search ADS PubMed 10 Scheppers E , van Dongen E , Dekker J , et al. Potential barriers to the use of health services among ethnic minorities: a review . Family Practice 2006 ; 23 : 325 – 48 . Google Scholar CrossRef Search ADS PubMed 11 Deblonde J , De Koker P , Hamers FF , et al. Barriers to HIV testing in Europe: a systematic review . Eur J Pub Health 2010 ; 20 : 422 – 32 . Google Scholar CrossRef Search ADS 12 European Commission . The state of men’s health in Europe. Extended report, 2011 . Available at: http://ec.europa.eu/health//sites/health/files/population_groups/docs/men_health_extended_en.pdf (1 January 2017, date last accessed) 13 Bertakis KD , Azari R , Helms LJ , et al. Gender differences in the utilization of health care services . J Family Practice 2000 ; 49 : 147 – 52 . 14 Fakoya I , Alvarez-Del Arco D , Monge S , et al. Advancing Migrant Access to Health Services in Europe (AMASE): protocol for a cross-sectional study . JMIR Res Protocol 2016 ; 5 : e74 . Google Scholar CrossRef Search ADS 15 Ravenell JE , Whitaker EE , Johnson WE Jr ., According to him: barriers to healthcare among African-American men . J Natl Med Assoc 2008 ; 100 : 1153 – 60 . Google Scholar CrossRef Search ADS PubMed 16 ACOG . ACOG Committee Opinion. Number 335, May 2006: the initial reproductive health visit . Obstetr Gynecol 2006 ; 107 : 1215 – 9 . CrossRef Search ADS 17 Guionnet A , Navaza B , Pizarro de la Fuente B , et al. Immigrant women living with HIV in Spain: a qualitative approach to encourage medical follow-up . BMC Pub Health 2014 ; 14 : 1115 . Google Scholar CrossRef Search ADS 18 Ministerio de la Presidencia y Para las Administraciones Territoriales . Boletín Oficial del Estado, 2016 . Available at: http://www.boe.es/diario_boe/txt.php? id=BOE-A-2016-7904 (1 March 2017, date last accessed) 19 Gagnon AJ , Carnevale F , Mehta P , et al. Developing population interventions with migrant women for maternal-child health: a focused ethnography . BMC Public Health 2013 ; 13 : 471 . Google Scholar CrossRef Search ADS PubMed 20 Roura M , Bisoffi F , Navaza B , et al. “Carrying Ibuprofen in the Bag” : Priority Health Concerns of Latin American Migrants in Spain—A Participatory Qualitative Study . PLoS One 2015 ; 10 : e0136315 . Google Scholar CrossRef Search ADS PubMed 21 Eurofound. Employment and working conditions of migrant workers-Spain, 2007 . Available at: http://www.eurofound.europa.eu/observatories/eurwork/comparative-information/national-contributions/spain/employment-and-working-conditions-of-migrant-workers-spain (1 January 2017, date last accessed) 22 Hacker K , Anies M , Folb BL , Zallman L . Barriers to health care for undocumented immigrants: a literature review . Risk Manag Healthc Policy 2015 ; 8 : 175 – 83 . Google Scholar CrossRef Search ADS PubMed 23 Navaza B , Guionnet A , Navarro M , et al. Reluctance to do blood testing limits HIV diagnosis and appropriate health care of sub-Saharan African migrants living in Spain . AIDS Behav 2012 ; 16 : 30 – 5 . Google Scholar CrossRef Search ADS PubMed 24 Sobrino-Vegas P , Rodriguez-Urrego J , Berenguer J , et al. Educational gradient in HIV diagnosis delay, mortality, antiretroviral treatment initiation and response in a country with universal health care . Antiviral Therapy 2011 ; 17 : 1 – 8 . Google Scholar CrossRef Search ADS 25 Del Amo J , Jarrin I , Garcia-Fulgueiras A , et al. Mental health in Ecuadorian migrants from a population-based survey: the importance of social determinants and gender roles . Soc Psychiatr Psychiatr Epidemiol 2011 ; 46 : 1143 – 52 . Google Scholar CrossRef Search ADS 26 Delara M . Social determinants of immigrant women's mental health . Adv Pub Health 2016 ; 2016 : 11 . 27 Stevens PE , Keigher SM . Systemic barriers to health care access for U.S. women with HIV: the role of cost and insurance . Int J Health Serv 2009 ; 39 : 225 – 43 . Google Scholar CrossRef Search ADS PubMed 28 Kushel MB , Gupta R , Gee L , Haas JS . Housing instability and food insecurity as barriers to health care among low-income Americans . J Gen Intern Med 2006 ; 21 : 71 – 7 . Google Scholar CrossRef Search ADS PubMed 29 UNAIDS . HIV, food security and nutrition . UNAIDS Policy Brief . 2008 . 30 Weiser SD , Fernandes KA , Brandson EK , et al. The association between food insecurity and mortality among HIV-infected individuals on HAART . J Acquir Immune Defic Syndr 2009 ; 52 : 342 – 9 . Google Scholar CrossRef Search ADS PubMed 31 Agudelo-Suarez AA , Ronda-Perez E , Gil-Gonzalez D , et al. The effect of perceived discrimination on the health of immigrant workers in Spain . BMC Pub Health 2011 ; 11 : 652 . Google Scholar CrossRef Search ADS 32 Williams DR , Sternthal M . Understanding racial-ethnic disparities in health: sociological contributions . J Health Soc Behav 2010 ; 51(Suppl) : S15 – 27 . Google Scholar CrossRef Search ADS PubMed 33 Earnshaw VA , Bogart LM , Dovidio JF , Williams DR . Stigma and racial/ethnic HIV disparities: moving toward resilience . Am Psychol 2013 ; 68 : 225 – 36 . Google Scholar CrossRef Search ADS PubMed 34 Fakoya I , Reynolds R , Caswell G , Shiripinda I . Barriers to HIV testing for migrant black Africans in Western Europe . HIV Med 2008 ; 9(Suppl 2) : 23 – 5 . Google Scholar CrossRef Search ADS PubMed 35 Perez-Molina JA , Pulido Ortega F . [Assessment of the impact of the new health legislation on illegal immigrants in Spain: the case of human immunodeficiency virus infection] . Enferm Infecc Microbiol Clin 2012 ; 30 : 472 – 8 . Google Scholar CrossRef Search ADS PubMed 36 Ministerio de Sanidad, Servicios Sociales e Igualdad. Boletín Oficial del Estado—Legislación Consolidada, 2012 . Available at: http://www.msssi.gob.es/profesionales/prestacionesSanitarias/CarteraDeServicios/docs/RDL_16_2012.pdf (1 January 2017, date last accessed). 37 Suess A , Ruiz Perez I , Ruiz Azarola A , March Cerda JC . The right of access to health care for undocumented migrants: a revision of comparative analysis in the European context . Eur J Public Health 2014 ; 24 : 712 – 20 . Google Scholar CrossRef Search ADS PubMed 38 Dang BN , Giordano TP , Kim JH . Sociocultural and structural barriers to care among undocumented Latino immigrants with HIV infection . J Immigr Minor Health 2012 ; 14 : 124 – 31 . Google Scholar CrossRef Search ADS PubMed 39 Deblonde J , Sasse A , Del Amo J , et al. Restricted access to antiretroviral treatment for undocumented migrants: a bottle neck to control the HIV epidemic in the EU/EEA . BMC Public Health 2015 ; 15 : 1228 . Google Scholar CrossRef Search ADS PubMed 40 UNAIDS . 90–90–90 . An ambitious treatment target to help end the AIDS epidemic, 2016 . Available at: http://www.unaids.org/sites/default/files/media_asset/90-90-90_en_0.pdf (1 January 2017, date last accessed). © The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved. 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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.
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1101-1262
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Abstract

Abstract Background In Spain, migrants are disproportionately affected by HIV and experience high rates of late diagnosis. We investigated barriers to health care access among migrants living with HIV (MLWH) in Spain. Methods Cross sectional electronic survey of 765 adult HIV-positive migrants recruited within 18 health care settings between July 2013 and July 2015. We collected epidemiological, demographic, behavioral and clinical data. We estimated the prevalence and risk factors of self-reported barriers to health care using multivariable logistic regression. Results Of those surveyed, 672 (88%) had information on health care access barriers: 23% were women, 63% from Latin America and Caribbean, 14% from Sub-Saharan Africa and 15% had an irregular immigration status. Men were more likely to report barriers than women (24% vs. 14%, P = 0.009). The main barriers were: lengthy waiting times for an appointment (9%) or in the clinic (7%) and lack of a health card (7%). Having an irregular immigration status was a risk factor for experiencing barriers for both men (OR: (4.0 [95%CI: 2.2–7.2]) and women (OR: 10.5 [95%CI: 3.1–34.8]). Men who experienced racial stigma (OR: 3.1 [95%CI: 1.9–5.1]) or food insecurity (OR: 2.1 [95%CI: 1.2–3.4]) were more likely to report barriers. Women who delayed treatment due to medication costs (6.3 [95%CI: 1.3–30.8]) or had a university degree (OR: 5.8 [95%CI: 1.3–25.1]) were more likely to report barriers. Conclusion Health care barriers were present in one in five5 MLWH, were more common in men and were associated to legal entitlement to access care, perceived stigma and financial constraints. Introduction Migrants accounted for 37% of the 29 747 new HIV diagnoses reported in the European Union/European Economic Area (EU/EEA) by the European Centre for Disease prevention and Control (ECDC) in 2015; and most of them (13%) were from Sub-Saharan Africa (SSA).1 The disproportionate burden of HIV infection experienced by migrants in Europe also shapes the Spanish HIV epidemic: 30% (n = 1037) of the 3428 new diagnoses reported in 2015 by the Spanish Surveillance Information System on new HIV diagnosis (SINIVIH) occurred in migrants, 237 (23%) of which were migrant women.2 Furthermore, in Spain people from Latin America and the Caribbean (LAC) not only account for the majority of the migrant population (37%)3 but are also the most relevant group among newly diagnosed HIV+ migrants (53%).2 Late HIV diagnosis is more frequent among migrants than among Spaniards,2 and this not only has a negative impact on the patient′s clinical outcomes but can also lead to increased HIV transmission rates within the community.4,,5 Several studies have shown how late HIV diagnosis among migrants is related to the presence of barriers to access of health promotion and health care services, including HIV testing, treatment and prevention.6 Migrants may be differently affected by health care barriers depending on their demographic profile (gender, country of origin, sexual orientation, etc.)2,,7 and on the socioeconomic vulnerabilities they experience.8 In Spain, these barriers might be larger for men and for migrants from non-Spanish speaking countries. In a Spanish study, high rates of health care barriers resulting in delayed HIV diagnosis occurred among SSA migrants7 while among people from LAC health care access challenges were reflected in an increased use of emergency services compared to native Spaniards.9 Cultural differences, poverty, uncertainty about health entitlements, lack of understanding of the health care system and fear of discrimination can all contribute to health care barriers.10 These barriers can occur at the patient level (sex, age, education, income, immigration status, personal health practices, etc.), the health care provider level (cultural sensitivity and competence, communication style, bilingualism, etc.) and the health system level (health policy, referrals, opening hours, waiting times, etc.).10,,11 Gender differences often influence health care access, especially among migrants: male migrants are often seeking care only in case of emergency,12 while female migrants are more likely to seek care via antenatal and child services.13 Research examining barriers to health care services among HIV-positive migrants population in Spain is scarce.6,,11 The current study aims to describe the nature and the prevalence of self-perceived barriers to access health care for migrants living with HIV (MLWH) in Spain, and assess risk factors associated with these barriers, while exploring gender differences. Methods aMASE The advancing Migrant Access to health Services in Europe (aMASE) study is a research project conducted within the European Network of Excellence for HIV cohort studies (EuroCoord). aMASE took place in 57 HIV clinics and hospitals across nine EU countries and its methodological design has been previously described.14 This paper focuses on the Spanish results of the study (aMASE-Spain). aMASE-Spain A cross-sectional electronic survey was conducted in 18 HIV clinics and hospitals across 7 Spanish regions, between July 2013 and July 2015. Participating sites are listed in the Annex (Annex 1). The research staff in each site identified and recruited eligible subjects among their HIV-positive patients who fulfilled the following criteria: (1) diagnosed within 5 years of the study date, (2) aged ≥18 years, (3) foreign-born, (4) resident in Spain for a minimum of 6 months and (5) able to complete, either alone or with assistance, a computer-assisted self-interview (CASI) in any of the 15 available languages (Amharic, Arabic, Dutch, English, French, German, Greek, Italian, Polish, Portuguese, Russian, Turkish, Tigrinya, Spanish and Somali). Sample size An overall effective sample size of 1600 individuals was pre-determined at the EU-level for the entire aMASE study.14 In Spain, 765 participants were recruited, which exceeded the minimum sample size required (N = 100) for the country. Using post-hoc analyses, we were able to determine that this sample size would provide a precision within 3.7% for our main estimate of interest. Sampling strategy Interested sites with the ability to recruit at least 40 migrants for the study, and with sufficient financial and human resources to conduct the survey, were invited to participate. A convenience sampling strategy was used to recruit study participants: eligible patients attending participating clinics were approached by research team members and invited to participate in the survey. Data collection We used two questionnaires: a self-administrated Patient Questionnaire (PTQ) (with socio-economic, epidemiological, clinical, behavioral, migratory and health services use data); and a Clinical Data Questionnaire (CDQ) completed by clinicians (CD4 count, viral load, HIV virus subtype). Both questionnaires were linked using an anonymous patient study number. Statistical analysis The primary outcome variable was defined by the presence of at least one self-reported barrier to health care services (‘Barriers in access to health care services’). Explanatory variables included sociodemographic and clinical characteristics such as: age, geographic origin, education, employment, income, HIV transmission route, immigration status (irregular immigrants were defined as: asylum seekers, refugees and undocumented migrants), late diagnosis (CD4 count <350 cells/mm3 at the time of diagnosis), perceived HIV stigma, perceived race stigma, perceived sexuality stigma, late diagnosis, AIDS at presentation, difficulty accessing food and delayed medical treatment due to medication costs. Separate analyses were carried out for men and women, since gender was conceptualized as an effect modifier for access to care.13 We calculated frequencies for categorical variables and median and interquartile range (IQR) for continuous ones. Bivariate associations between exposure variables and the variable of interest were calculated. Risk factors for health care barriers were identified through logistic regression. Variables with a P values lower than 0.10 in bivariate analysis were used to fit multivariable models for both men and women. We constructed the final model through a forward procedure and the best model was selected comparing maximum likelihood values. We calculated odds ratios (ORs) with their 95% confidence intervals (95% CI) for each variable included in the final model. Statistical significance was evaluated using the Wald test and all analyses were performed using STATA software (Version 14.0, College Station, Texas). Ethical committee clearance The study received approval from the Ethics Committee of Carlos III Spanish Health Institute and from some of the ethics boards of the hospitals/clinics conducting the study. All participants consented to take part in the study. Results Sociodemographic and clinical characteristics In total, 775 individuals were interviewed. We excluded 93 (12%) individuals who had no information on the primary outcome of interest. We also excluded 10 (1%) transgender individuals because their number was too small to include in a stratified analysis and draw meaningful conclusions. Table 1 summarizes the sociodemographic characteristics of the 672 participants included in the analysis. Among those, 154 (23%) were female and 410 (61%) self-identified as men who have sex with men (MSM). Among women, 50% were from LAC and 31% from SSA, while among men, 66% were from LAC and 11% from Western Europe. Non-regular migrants constituted 15% of the sample; this proportion was higher among women (21%) and people from SSA (33%). Migrants from SSA were more likely to be diagnosed late (65%) and among LAC men, 74% identified themselves as MSM (data not shown). Table 1 Descriptive characteristics of the study participants by sex Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Table 1 Descriptive characteristics of the study participants by sex Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Characteristics Female (N = 153) Male (N = 519) Total (N = 672) P-value Age (years) 0. 767 Age (median, IQR) 35[29–42] 35[29–42] 35[29–42] Geographical origin 0. 000 Western Europe 6 (4%) 58 (11%) 64 (10%) Central and Eastern Europe 19 (12%) 37 (7%) 56 (8%) Sub-Saharan Africa 47 (31%) 45 (9%) 92 (14%) Latin Am. and the Caribbean 76 (50%) 345 (66%) 421 (63%) Others 5 (3%) 34 (7%) 39 (6%) Education 0. 000 Primary 41 (27%) 59 (11%) 100 (15%) Secondary 80 (52%) 224 (43%) 304 (45%) University 32 (21%) 236 (45%) 268 (40%) Employment 0. 011 Working 69 (45%) 297 (57%) 366 (54%) Unemployed 52 (34%) 155 (30%) 207 (31%) Other 32 (21%) 67 (13%) 99 (15%) Income 0. 000 Above minimum wage 18 (12%) 196 (38%) 214 (32%) Minimum wage 24 (16%) 94 (18%) 118 (18%) Below minimum wage 52 (34%) 108 (21%) 160 (24%) Do not earn own wage 47 (31%) 99 (19%) 146 (22%) Unknown 12 (8%) 22 (4%) 34 (5%) Immigration status 0. 014 National/Residents 121 (79%) 452 (87%) 573 (85%) Irregular status 32 (21%) 67 (13%) 99 (15%) Transmission mode 0. 000 Heterosexual 150 (98%) 91 (18%) 241 (36%) Men who have sex with men - 410 (79%) 410 (61%) Injecting drug users 3 (2%) 18 (3%) 21 (3%) Late HIV diagnosis 0. 000 Yes 83 (54%) 191 (37%) 274 (41%) No 66 (43%) 290 (56%) 356 (53%) Unknown 4 (3%) 38 (7%) 42 (6%) Perceived discrimination based on HIV stigma 0. 017 Yes 17 (11%) 46 (9%) 63 (9%) No 131 (86%) 470 (90%) 601 (89%) Unknown 5 (3%) 3 (1%) 8 (1%) Perceived discrimination based on race stigma 0. 609 Yes 33 (21%) 132 (25%) 165 (24%) No 119 (78%) 383 (74%) 502 (75%) Unknown 1 (1%) 4 (1%) 5 (1%) Perceived discrimination based on sexual orientation 0. 000 Yes 2 (1%) 82 (16%) 84 (13%) No 150 (98%) 433 (83%) 583 (87%) Unknown 1 (1%) 4 (1%) 5 (1%) Forced sex 0. 000 Yes 28 (18%) 45 (9%) 73 (11%) No 118 (77%) 466 (90%) 584 (87%) Unknown 7 (5%) 8 (1%) 15 (2%) Delayed medication due to cost 0. 299 No 11 (7%) 26 (5%) 37 (6%) Yes 142 (93%) 493 (95%) 635 (94%) Difficulty accessing food 0. 361 Yes 44 (29%) 120 (23%) 164 (24%) No 106 (69%) 388 (75%) 494 (74%) Unknown 3 (2%) 11 (2%) 14 (2%) Self-perceived barriers to health care access One in five participants reported experiencing at least one barrier to accessing health care in Spain (22%) (table 2). Men (24%) were more likely to report barriers than women (14%).The top three self-perceived barriers to accessing health care were the same in males and females: waiting time for an appointment, lacking a health card and waiting times in clinic. Barriers were more common among migrants from SSA (27%) and in those from other geographical origins (North Africa, North America, Asia and Oceania) (36%), compared to the rest of the participants, notably Spanish speaking migrants. Migrants from these first two groups, mostly experienced barriers related to language, waiting time in the clinic and difficulty finding a general practitioner (GP) (data not shown). Finally, rate of self-perceived barriers to health care was highest among persons who inject drugs (PWID) (38%) compared to heterosexuals (HTX) (18%) and MSM (24%). Migrants who inject drugs were more likely to perceive waiting time for appointment (14%) and uncertainty towards health entitlements (24%) as barriers to health care access (data not shown). Table 2 Prevalence of self-perceived barriers to health care access and type of barriers experienced by sex Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 * At least one barrier to access health care. Table 2 Prevalence of self-perceived barriers to health care access and type of barriers experienced by sex Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 Barriers Female (N = 153) Male (N = 519) Total (N = 672) P-value Barriers to health care* 22 (14.4%) 126 (24.3%) 148 (22%) 0.009 Types of barriers Waiting time for appointment 9 (5.9%) 49 (9.4%) 58 (8.6%) 0.168 No health card 6 (3.9%) 43 (8.3%) 49 (7.3%) 0.068 Waiting time in clinic 10 (6.5%) 39 (7.5%) 49 (7.3%) 0.682 Unsure health rights 1 (0.7%) 22 (4.2%) 23 (3.4%) 0.032 Language barriers 4 (2.6%) 16 (3.1%) 20 (3%) 0.764 Opening hours 1 (0.7%) 11 (2.1%) 12 (1.8%) 0.229 Confidentiality - 8 (1.5%) 8 (1.2%) 0.122 Other 1 (0.7%) 4 (0.8%) 5 (0.7%) 0.882 Difficulty finding GP - 2 (0.4%) 2 (0.3%) 0.442 * At least one barrier to access health care. Determinants of barriers to health care access Univariate analysis determinants of barriers to health care access for men and women are presented in table 3. Among women, health care access barriers were more likely to be perceived in those with university education, an irregular immigration status and medication delay due to treatment cost. Among men, barriers were associated with being from SSA, having a primary or university education, not earning their own wage, having an irregular immigration status, experiencing difficulties accessing food, delaying medical treatment due to medication costs and being stigmatized for their race, sexuality or HIV-positive status. Overall 14% of women and 25% of men who reported barriers to health care were diagnosed late for HIV (CD4 count at diagnosis <350 cells/mm3). Table 3 Prevalence and risk factors of barriers to access health care services by sex (Univariable analysis) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Table 3 Prevalence and risk factors of barriers to access health care services by sex (Univariable analysis) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) Sociodemographic characteristics Female (N = 153) Male (N = 519) Prevalence [n (%)] [OR (95%CI)] Prevalence [n (%)] Male [OR (95%CI)] Total prevalence 22 (14%) 126 (24%) Age p-value 0.921 0.132 18–30 years 7 (32%) 1 43 (34%) 1 31–40 years 8 (36%) 0.80 (0.27–2.39) 53 (42%) 0.94 (0.59–1.51) ≥41 years 7 (32%) 0.90 (0.29–2.81) 30 (24%) 0.61 (0.36–1.04) Geographic origin p-value 0.990 0.013 Latin America and Caribbean (LAC) 12 (16%) 1 73 (21%) 1 Central and Eastern (CEE) 3 (16%) 1.00 (0.25–3.97) 7 (19%) 0.87 (0.37–2.06) Sub-Saharan Africa (SSA) 7 (15%) 0.93 (0.34–2.57) 18 (40%) 2.48 (1.30–4.76) Western Europe (WE) - - 14 (24%) 1.19 (0.62–2.28) Others - - 14 (41%) 2.61 (1.27–5.41) Education p-value 0.041 0.001 Primary or none 6 (15%) 1 18 (31%) 1 Secondary 7 (9%) 0.56 (0.17–1.79) 37 (17%) 0.45 (0.23–0.87) University 9 (28%) 2.28 (0.72–7.27) 71 (30%) 0.98 (0.53–1.82) Employment p-value 0.386 0.000 Working 7 (10%) 1 60 (20%) 1 Unemployed 9 (17%) 1.85 (0.64–5.35) 36 (23%) 1.19 (0.75–1.91) Other (Student, Retired, etc.) 6 (19%) 2.04 (0.63–6.67) 30 (45%) 3.2 (1.83–5.6) Income p-value 0.205 0.002 Above minimum wage 1 (6%) 1 36 (18%) 1 Minimum wage 1 (4%) 0.74 (0.04–12.67) 20 (21%) 1.2 (0.65–2.22) Below minimum wage 8 (15%) 3.09 (0.36–26.61) 24 (22%) 1.27 (0.71–2.27) Do not earn own wage 9 (19%) 4.03 (0.47–34.34) 40 (40%) 3.01 (1.76–5.17) Unknown 3 (25%) 5.67 (0.51–62.66) 6 (27%) 1.67 (0.61–4.56) Immigration status p-value 0.001 0.000 National/Resident 11 (9%) 1 91 (20%) 1 Irregular 11 (34%) 5.23 (2.01–1.36) 35 (52%) 4.34 (2.55–7.38) HIV transmission route p-value 0.403 0.374 Heterosexual 21 (14%) 1 22 (24%) 1 Men who have sex with men - - 97 (24%) 0.97 (0.57–1.65) Injecting drug users 1 (33%) 3.07 (0.27–35.39) 7 (39%) 1.99 (0.69–5.77) Late HIV diagnosis (CD4<350 cells/mm3) p-value 0.923 0.723 Yes 12 (14%) 1 47 (25%) 1 No 9 (14%) 1.04 (0.41–2.66) 71 (24%) 0.93 (0.6–1.42) Difficulty accessing food p-value 0.082 0.000 No 12 (11%) 1 76 (20%) 1 Yes 10 (23%) 2.30 (0.91–5.82) 45 (38%) 2.46 (1.58–3.85) Perceived discrimination based on HIV stigma p-value 0.313 0.000 No 18 (14%) 1 102 (22%) 1 Yes 4 (24%) 1.93 (0.56–6.58) 23 (50%) 3.61 (1.94–6.69) Perceived discrimination-based on race stigma p-value 0.087 0.000 No 14 (12%) 1 71 (19%) 1 Yes 8 (24%) 2.40 (0.91–6.34) 54 (41%) 3.04 (1.97–4.68) Perceived discrimination based on sexual orientation p-value 0.231 0.001 No 21 (14%) 1 94 (22%) 1 Yes 1 (50%) 6.14 (0.37–102.02) 32 (39%) 3.04 (1.97–4.68) Forced sex p-value 0.826 0.037 No 15 (13%) 1 108 (23%) 1 Yes 4 (14%) 1.14 (0.35–3.76) 17 (38%) 2.01 (1.06–3.82) Delayed medication (medication cost) p-value 0.043 0.013 No 18 (13%) 1 114 (23%) 1 Yes 4 (36%) 3.94 (1.05–14.8) 12 (46%) 2.85 (1.28–6.33) The multivariable logistic analysis (table 4) shows the adjusted risk factors associated with barriers to health care access. For women, these risk factors included: having an irregular immigration status; having a university degree and delaying non-HIV medical treatment due to medication costs. Among men we identified the following risk factors: irregular immigration status, difficulty accessing food and experiencing racial stigma. Table 4 Factors associated with barriers in access to health care services among women and men (adjusted model) Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Table 4 Factors associated with barriers in access to health care services among women and men (adjusted model) Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Variables Women Men OR [95% CI] OR [95% CI] Immigration status National/resident 1 1 Irregular 10.5 [3.1–34.8] 4 [2.2–7.2] Education Primary or none 1 1 Secondary 0.7 [0.2–2.6] 0.7 [0.3–1.4] University 5.8 [1.3–25.1] 1.7 [0.8–3.5] Delayed medication (medication costs) No 1 - Yes 6.3 [1.3–30.8] Geographic origin Western Europe - 1 Central and Eastern Europe 0.6 [0.2–1.8] Sub-Saharan Africa 1.4 [0.5–3.6] Latin America and Caribbean 0.6 [0.3–1.1] Others 1.5 [0.6–4.2] Difficulty accessing food No - 1 Yes 2.1 [1.2–3.4] Race stigma No - 1 Yes 3.1 [1.9–5.1] Discussion In this study, we found that approximately one in seven migrant women and one in four migrant men living with HIV in Spain, and already linked to HIV care, reported having previously experienced at least one barrier to access health care. The nature, the prevalence and the risk factors of these barriers are different among men and women and reflect the extreme social and economic vulnerability experienced by a relevant proportion of HIV positive migrants living in Spain. We found that HIV positive migrant men living in Spain were more likely to report health care barriers compared to women. This finding is in accordance with results from a study conducted in the United Kingdom by Burns et al.8 The limited engagement of men in health care may reflect their propensity to perceive physicians as emergency care providers rather than continuous care providers, thus only seeking help for complex health issues,12,,15 On the other hand, women’s entry into the health care system is facilitated by their role as caregiver accompanying relatives or through reproductive and child health care services.16 Despite beneficiating from this ‘front door’ into the health care system, women often face other types of barriers that hinder their access to care. For instance, a study by Guionnet et al. reported that HIV-positive migrant women living in Spain often perceived their condition as lethal and financially unaffordable17 even though HIV combination antiretroviral therapy (cART) is free for all people living with HIV residing in Spain, regardless of their legal status.18 This could either reflect migrant women’s limited knowledge of their health care entitlements or them prioritizing their work and providing for their families over attending clinic visits during working hours.19,,20 Several structural barriers to care were identified in this study. Extended waiting times for appointment and during clinic visits were the most commonly reported barriers for both male and female migrants. Although the sample size for women was small, these results support previous work that has been done in this area21 and suggest that the rigid schedules of health care centers do not fit migrants’ demanding work conditions and timetables. This was corroborated in studies showing that migrants perceive occupational strains linked to job insecurity and inflexible work shifts as a major impediment to their health and are often unable to access care during work hours due to fear of losing their employment.20,,22 In our study, we also found that migrants from SSA were more likely to experience barriers, especially language barriers, which could eventually exacerbate their social exclusion and lead to their inability to understand the health care system and their health care rights entitlements.23 On the other hand, migrants from LAC, who are native Spanish speakers, did not report any language barrier. We identified gender specific determinants of health care barriers. For women, education and financial considerations were specific risk factors for barriers. We found that women with a university degree were more likely to experience barriers to health care and were also less likely to have children, which may cause them to under use antenatal services.24 Highly educated migrant Ecuadorian women who had to accept jobs for which they are over qualified have been reported to experience poor mental health outcomes,25 which combined with financial strains and occupational dissatisfaction may exacerbate pre-existing health care barriers. Women who delayed non-HIV treatments due to financial restrictions were also more likely to report health care barriers, thus highlighting a subgroup of women who are particularly vulnerable as they generally earn less income and have a lower rate of employment than men.26,,27 Men-specific risk factors included difficulty accessing food and racial stigma. Limited food access has been previously associated with low health prioritization and an increased need to focus on food and housing procurement.28 Furthermore, in those living with HIV, food insecurity is also a risk factor for treatment interruption, morbidity and mortality.29,,30 As for racial stigma, this is a well-documented risk factor for disparities in health access.31,,32 In people at risk of or living with HIV, perceived racial stigma has been associated with health care barriers such as: lower trust in health care provider, higher likelihood of missing medical appointments, fear of HIV testing and lower adherence to cART.33,,34 In both migrant men and women, having an irregular immigration status was the main risk factor for experiencing barriers. In 2012, Spain revoked free and universal access to the health care system for undocumented migrants, with some exceptions such as for conditions of public health relevance which include HIV infections.35,,36 Although these rights were partly returned in late 2015, irregular migrants are still denied access to the health card.18 National policies restricting health care access for undocumented migrants have an important negative impact on migrant health as has been widely acknowledged by ECDC and WHO.22,,37 HIV-positive migrants are particularly vulnerable due to HIV social stigma and the chronic nature of the infection, which requires life-long treatment and care.38,,39 Governments should guarantee free access to health care system regardless of migrants’ legal status in order to ensure the timely access to treatment, thus resulting in better clinical outcomes for the patient and decreased HIV transmission at community level.39 Restricting health care access for undocumented migrants not only hampers public health measures but also goes against the fundamental principles of equity in health care access outlined in the international human rights laws which have been ratified by all EU/EEA countries.39 Facilitating health care access to undocumented immigrants is therefore a human rights obligation. We would like to acknowledge some study limitations. First, the recruitment of our sample from HIV health facilities has led to an underrepresentation of the most vulnerable patients who still have not accessed the health care system, or are still unaware of their HIV status. It is expected that barriers would be more extreme in this group. Second, there may have been an information bias due to the sensitivity of the information collected (e.g. legal status, sexual orientation, and so on), which we minimized through the migrant-friendly nature of the recruiting centres and by privacy provided by self-reporting to electronic questionnaires. Thirdly, we could not compare the prevalence or effect of barriers between immigrants and native Spaniards, due to the lack of a control group. Finally, due to insufficient sample size, our analysis among women is underpowered, which may lead to imprecision in our estimates within this group. Sample size restriction also made it difficult to assess the risk factors for health care barriers according to specific geographical origins. In spite of this limitation, we have chosen to stratify findings by sex given that findings show clear gender differences that would otherwise become invisible. Conclusions The current findings indicate that barriers to health care are common among HIV-positive migrants living in Spain linked to care. Defining and understanding these barriers are therefore key to the development of policies and effective care programs tailored to reach HIV-positive migrants. New health strategies should aim to reduce structural barriers to health care access, especially for vulnerable groups such as undocumented migrants. This is also crucial for achieving UNAIDS 90–90–90 targets with the ultimate goal to control the HIV epidemic by 2020.40 Acknowledgements This research would not have been possible without the collaboration of the patients, physicians, nurses, data managers and researchers who have taken part in this project. The aMASE Research Group Anne-Françoise Gennotte, Cécile La Morté Barthélemy, Claudia Wengenroth, Giota Touloumi, Gountas Ilias, Koulai Loumpiana, Anagnostou Olga, Gundolf Schüttfort, Claudia Wengenroth, Henrique Barros, Paula Meireles, Julia del Amo, Débora Álvarez, Susana Monge, Bruno Ledergerber, Cornelia Staehelin, Maria Prins, Janneke Bill, Freke Zuure, Tullio Prestileo, Nicolla Petrosillo, Fiona Burns, Ibi Fakoya, Jorge del Romero J, Carmen Rodríguez, Mar Vera, Isabel Río, Vanessa Paredes, Nieves Sanz, Fernando Dronda, María Velasco Arribas, María Gracia Mateo, Mar Gutiérrez, Pere Domingo, Hernando Knobel, Teresa Pellicer, Emma Fernández, María del Carmen Ligero, Margarita Robau, José María Miró, Inmaculada Ocaña, Joaquín Burgos, María José Barberà, Maider Arando, Félix Gutiérrez, Mar Masía, José Ramón Blanco, Josefina Belda-Ibáñez, Elisa Fernández García, Trinidad Zafra Espinosa, Ana López-Lirola, Miguel Salavert, Marta Montero, Eva Calabuig, Sandra Cuellar, Pompeyo Vinciana, Reyes Palacios, Federico García, Alejandro Peña, José Antonio Iribarren, Mirian Aguado, Federico Pulido, Ángel Portillo and José Luis Gómez-Sirvent. Funding This work is part of aMASE (Advancing Migrant Access to Health Services in Europe) which was funded by the European Union's Seventh Framework Programme for research, technological development and demonstration under EuroCoord grant agreement n° 260694. This work has been supported by the Spanish Medical Fund Research (PI12/02134) and Spanish Network of Excellence on HIV (RD12/0017/0018, RD16CIII/0002/0006), FIPSE (Fundación de Investigación y Prevención del SIDA) and CIBERESP. This research would not have been possible without the collaboration of the patients, physicians, nurses, data managers and researchers who have taken part in this project. Conflicts of interest: None declared. Key points 1 in 5 HIV-positive migrant living in Spain and linked to care experience barriers to healthcare access. HIV-positive male migrants are more likely to report barriers than their female counterpart. 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This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Published: Jan 8, 2018

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