Received date: July 18, 2012; Accepted date: August 11, 2012; Published date: August 14, 2012
Citation: Saroha E, Qureshi N (2012) Targeted Interventions in HIV/AIDS and Gender Disparities in Health Services Utilization among HIV Infected of Delhi, India. J Community Med Health Educ 2:168. doi: 10.4172/2161-0711.1000168
Copyright: © 2012 Saroha E, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Objective: In India 2.5 million people are positive for HIV/AIDS (PLHIV) where men population is more than women. Targeted intervention overlooks gender variations and focuses on “high risk groups”. Gender disparities are lesser known. Objective of this study was to examine HIV/AIDS healthcare services utilization disparities among male, female, and ‘other’ PLHIVs in Delhi, India.
Methods: Data from a cross-sectional study for a convenience sample of 355 adult PLHIVs were analyzed in 2011. Chi-square test, ANOVA, and multivariable logistic regression helped determine odds of HIV/AIDS healthcare services utilization by male and female PLHIVs compared to ‘other’ PLHIVs.
Results: Male PLHIVs were less likely to use pre-test counseling (ORa=0.18, 95% CI: 0.03, 0.96, p<0.05), treatment for any STI (ORa=0.30, 95% CI: 0.12, 0.73, p<0.05), and free condoms (ORa=0.24, 95% CI: 0.07, 0.80, p<0.05), than ‘other’ PLHIVs. Contrarily, male PLHIVs were 3 times (ORa: 3.29, 95% CI: 1.37, 10.87, p<0.05) more likely to get treated for any opportunistic infections than ‘other’ PLHIVs. Female PLHIVs were less likely to utilize pretest counseling (ORa: 0.16, 95% CI: 0.03, 0.70, p<0.05) and free condoms (ORa: 0.06, 95% CI: 0.01, 0.25, p<0.05) than ‘other’ PLHIVs.
Conclusions: Utilization of HIV/AIDS healthcare services varied by gender among adult PLHIVs of Delhi.
Targeted intervention strategy in India augment gender disparities in HIV/AIDS healthcare and inhibit utilization among male and female PLHIVs. Universal access can foster gender equity.
Targeted interventions; HIV/AIDS; Gender; Utilization; India
India has one of the highest numbers of HIV-infected people in the world [1-5]. HIV transmission in India is largely through sexual route (80%), not only heterosexual but also homosexual and bisexual [3,5-10]. Additionally, approximately 8-70% adults engage in paid or unprotected sex as noted in different surveys and studies of India [7,9-13]. According to the National AIDS Control Organization (NACO)-2006, adult HIV prevalence in India is estimated to be 0.36% . HIV prevalence is higher among adult men (0.43%) than adult women (0.29%) [1,2,5]. Total number of People Living with HIV/ AIDS (PLHIVs) in the country is about 2.47 million [1,2]. In country’s capital, Delhi, HIV prevalence is 0.27% and as many as 30,000 adult PLHIVs are estimated to be living in Delhi [2,4,5,14].
Delhi is a fast growing city which has expanded to neighboring towns such as Faridabad and Gurgaon in the State of Haryana, and Noida and Ghaziabad in the State of Uttar Pradesh, together this area is defined as Delhi-National Capital Region (NCR). HIV/AIDS programs and services in Delhi are coordinated through the ‘Delhi State AIDS Control Society (DSACS)’ under the direction of NACO [3,14]. As of today DSACS has 84 ‘Integrated Counseling and Testing Centers’ and a wide network of non-governmental organizations where PLHIVs receive free services such as pre-post test counseling, Antiretroviral Therapy (ART) counseling and ART, lab tests, treatment for Opportunistic Infections (OIs) and Sexually Transmitted Infections (STIs), nutritional counseling, psychosocial counseling, counseling for positive living, condoms, etc. . SACS in Haryana and Uttar Pradesh offer similar services and PLHIVs of Delhi-NCR can approach any SACS as per their convenience.
Since early 1990s NACO had targeted intervention for prevention and control of HIV/AIDS epidemic in the country [4,8,9]. Certain population segments such as ‘Men Who Have Sex With Men’ (MSM) are identified and targeted as “High Risk Groups” (HRGs) [2,7,9,15]. About 29,000 MSM live in Delhi and in 2006 it was estimated that HIV prevalence among MSM of Delhi (12.27%) was higher than the national average (6.41%) [1,2,5,6,14]. It is argued that such profiling and segmentation will enhance effectiveness of intervention [2,4,7,9,15]. On the contrary, evaluation of second phase of the ‘National AIDS Control Program’ (NACP) highlighted severe limitations of such targeted approach. This strategy was found to have limited success in HIV prevention due to inadequate focus on male clients, rigidity in service delivery, few options for treatment of STIs for women, and limited evaluation of effectiveness [9,16,17]. It is known that targeted approach for an already stigmatized disease reiterates the stigma and hampers HIV care among the so called “general population” [7,15,18-20].
Gender stratified HIV data are restricted to the male-female dyad. Alternative gender identities such as homosexual, gay, lesbian, transgender, bisexual, eunuch, hijra, kothi, double-decker, kinnar, panthi, etc., are widely documented in various studies from across the country [6,12,21-27]. These alternative gender identities are overlooked and not factored-in in the dyadic gender stratified reports of any of our healthcare programs including HIV/AIDS. Classification of alternative gender identities-on pretext of distinctive characteristics of each identity- is considered to be a tedious and futile exercise unworthy of program planners’ attention. Even so, recently in the State of Tamil Nadu people with alternative gender identities offered to identify themselves as ‘other’ gender, clearly underrating the presumed complexity of classification . Such self-opted categorization by people with alternative gender identities under ‘other’ is probably far more sensitive and less stigmatizing than NACO’s categorization of this group as MSM and HRG. NACO supposedly caters to the HIV healthcare needs of almost all who have alternative gender identities through interventions that are designed for MSMs or HRGs. As a result, HIV data for MSM are either available separately or along with other HRGs such as female sex workers and injecting drug users [2,3,29]. Note that, all individuals who have alternative gender/sexual groups do not identify themselves as MSM or HRG [6,21,22,24]. It is erroneous and insensitive to presume that all individuals with alternative gender identities are MSM and HRG. Prejudices of policy makers have resulted in design of programs where health status of people of alternative gender identities can neither be ascertained nor compared with conventional male-female gender dyad.
Health programs, specifically those pertaining to reproductive and sexual health, in India have traditionally targeted women [30-33]. Consequently, men were neglected and they sparingly sought healthcare for reproductive and sexual health [7,30-36]. In mid-1990’s a need was felt to involve men as equal partners in reproductive and sexual health [30-36]. Despite substantial proportion of men being infected and affected by various reproductive and sexual health problems approximately <40% benefit from existing health programs [31,33,35,36]. Though women have been targeted all along, yet very few (approximately 10-60%) benefit due to stigma around sex and due to the subservient position of women in our society [10,15,18,19,37-41]. Little information is available about the profile of beneficiaries of HIV/ AIDS healthcare services. Data on utilization of HIV/AIDS healthcare services are sparse and deficient. Countrywide, 77% PLHIVs, more men (62%) than women (38%), are known to be on ART . DSACS estimates that 16% adult PLHIVs receive free ART in Delhi [2,4,5,14]. Fewer women (<40%) are known to access pre-test counseling and compared to men more women (approximately 65%) attend STI clinics .
There is evidence that HIV has heightened vulnerabilities and the subsequent burden to seek healthcare among those infected and affected by the virus- targeted by interventions or otherwise [7,13,15,41,43]. Third phase of NACP continues to concentrate on targeted intervention but at the same time aims at universal access to HIV care and reduction of gender-specific health disparities [3,9,43-45]. Gender and gender dynamics impact healthcare in general and utilization of HIV care in particular [44,46,47]. Till date, gender segregated HIV data in India fail to contrast HIV care disparities among male, female, and people who have alternative gender identities (‘other’). There is a need to transit from targeted approach and to document gender disparities in order to commence universal access to HIV care in the country [15,43,47-49]. In the context of the targeted intervention strategy of NACO and diverse gender identities unique to our country, we examined gender disparities in HIV healthcare services utilization among PLHIVs who identify as male, female, or ‘other’ gender in Delhi-NCR, India.
Gender disparities in HIV healthcare services utilization were studied in Delhi-NCR. A cross-sectional study with 355 adult PLHIVs was conducted during March-July 2011. Research team of Saksham project, Jamia Millia Islamia, New Delhi sought assistance for sample selection and data collection from a non-governmental organization called Nai Umang Positive Welfare Society (NUPWS) which caters to PLHIVs in Delhi-NCR. This was necessary to ensure confidentiality and protection of study subjects. Study proposal was approved by the Research Ethics Committee of the ‘Tata Institute of Social Sciences’, Mumbai, India. NUPWS identified and recruited eligible study sample. Informed consent was sought prior to the interview. Data submitted by NUPWS to Saksham had no personal identifiers. Likewise, NUPWS did not retain any filled questionnaires which were a link between respondent and data.
NUPWS was directed to recruit convenience sample for this study. Since HIV prevalence is disproportionate across genders (approx. men=45%, women=30%, transgender=15%) a disproportionate sample (~5:3:1) was aptly estimated. For sample size calculation we had set α at 0.05 and 1-β at 0.80. We hypothesize 60% utilization of HIV/ AIDS healthcare services among PLHAs. Hypothesized odds ratio among cases relative to controls was 0.50. Taking into consideration an attrition rate of 20%, disproportionate sample of male (n=210), female (n=105), and other (n=40), resulting in to total sample size of N=355 PLHAs was arrived. PLHIVs who had been HIV positive and on ART before 1 January 2009 (as indicated in Doctor’s or ART prescription/lab report) were recruited at various sites of utilization of HIV/AIDS healthcare. PLHIVs who were not residents of Delhi-NCR, not associated with NUPWS, incapable of giving consent, and minors (≤ 17 years) were excluded from the sample.
The outcome of interests was utilization of any of the following HIV/ AIDS health services ever since the respondent became HIV positive: HIV pre/post-test counseling, nutrition counseling, psychosocial counseling, positive living counseling, free condoms, treatment for OIs and STIs. Utilization was defined as actual receipt of services by PLHIVs either from government run facility/provider or private/nongovernment facility/provider, as reported by the PLHIV. Responses were coded as No=0 and Yes=1. The independent variable was ‘gender identity’ of PLHIV, taking into account not only male-female dyad but also alternative gender identities that are common in India. Although NUPWS had made certain presumptions regarding the gender identity of potential respondents to facilitate sample recruitment but during the interview NUPWS further probed PLHIVs to ascertain their gender identity. During the interview, PLHIVs were asked to specify their gender identity: a) assigned to them by their parents when they were young, b) gender identity they would like to indicate on a form at a healthcare facility, c) gender identity they would like the healthcare provider to address them with, and d) genital organs that they were born with. Differences in proportion for each response option were computed and κ-statistics was used to determine level of agreement with presumptions made by NUPWS. High level of agreement (κ>0.80, p<0.05) with NUPWS presumptions was found therefore gender identity assigned by NUPWS (coded as ‘other’=0, female=1, male=2) was bases of further analysis (Table 1). Data on socio-demographic factors such as age, number of years since HIV +ve, marital status, children, religion, caste/tribe, living situation, education, employment, monthly income, and distance to HIV care facility from home were also collected.
|Male/Son/Penis (n, %)||249, 70.14%||210, 59.15%||212, 59.72%||218, 61.41%||226, 63.66%|
|Female/Daughter/Vagina (n, %)||106, 29.86%||105, 29.58%||108, 30.42%||123, 34.65%||105, 29.58%|
|Other/Transgender/Not defined (n, %)||0||40, 11.27%||35, 9.86%||14, 3.94%||24, 6.76%|
|Κ-statistics, p-value||N/A||Ref||0.974, <0.05||0.862, <0.05||0.915, <0.05|
Table 1: Gender identity (assigned, preferred, biological) of adult PLHIVs in Delhi-NCR, Jul 2011 (N=355).
Data were collected through an interviewer-administered structured questionnaire which was field tested on a disproportionate sample of 40 PLHIVs in February 2010. Data were entered and analyzed using SPSS 13.0 . Chi-square test and ANOVA were used to identify socio-demographic differences between 3 gender groups. Chi-square test was also used to assess association between gender and utilization of HIV/AIDS healthcare. Pearson and Spearman Rank correlation analysis were done to identify strongly correlated (r ≥ 0.80) socio demographic factors. Multivariate logistic regression analyses  was used to determine the odds of lower utilization of healthcare services by male or female PLHIVs compared to ‘other’ PLHIVs. A one-tailed test with α=0.05 and, p<0.05, 95% Confidence Interval (CI) was used. Two types of logistic regression model were built: unadjusted/crude and adjusted/reduced model (adjusted for significant socio-demographic variables).
In this sample of 355 adult PLHIVs of Delhi-NCR, average age was 35 years and mean years since HIV +ve was 4 years. Majority of PLHIVs have been married with children, Hindu, not belonging to reserved caste/tribe category, having only school (grade 1-12) education, employed in private sector, earning <Rs.5000/- (approx. USD100) per month, and using HIV healthcare facility that was more than 10 km away from their residence. This socio-demographic profile significantly varied by gender identity (p<0.05). Greater proportion of female and ‘other’ PLHIVs were unemployed compared to male PLHIVs. During the interview some PLHIVs said that since they were unemployed they had to make a living through begging and sex work. Monthly income of female and ‘other’ PLHIVs was less than that of male PLHIVs. Whereas, majority of ‘other’ PLHIVs sought HIV care at a facility that was 0-10 km away from their home, greater proportion of male and female PLHIVs visited HIV care facility far away from home (>10 km) (Table 2). These socio-demographic factors were weakly correlated (r<0.80).
|Mean age (Range, SD)||34.68
|Mean no. of yrs since HIV +ve (Range, SD)||4.00
|Ever married||282, 79.44%||163, 57.80%||105, 37.23%||14, 4.96%|
|Never married||73, 20.56%||47, 64.38%||0||26, 35.62%||<0.05|
|Yes||251, 70.70%||145, 57.77%||93, 37.05%||13, 5.18%|
|No||104, 29.30%||65, 62.50%||12, 11.54%||27, 25.96%||<0.05|
|Lives in a…|
|Nuclear family||200, 56.34%||124, 62.00%||59, 29.50%||17, 8.50%|
|Joint family||155, 43.66%||86, 55.48%||46, 29.68%||23, 14.84%||0.15|
|Hindu||299, 84.23%||186, 62.21%||93, 31.10%||20, 6.69%|
|Not-Hindu||56, 15.77%||24, 42.86%||12, 21.43%||20, 35.71%||<0.05|
|Belongs to reserved caste/tribe|
|Yes||100, 28.17%||57, 57.00%||41, 41.00%||2, 2.00%|
|No||255, 71.83%||153, 60.00%||64, 25.10%||38, 14.90%||<0.05|
|College & above||13, 3.66%||10, 76.92%||1, 7.69%||2, 15.38%|
|10-12th grade||102, 28.73%||66, 64.71%||26, 25.49%||10, 9.80%|
|<10th grade||173, 48.73%||105, 60.69%||55, 31.79%||13, 7.51%|
|Illiterate||67, 18.87%||29, 43.28%||23, 34.33%||15, 22.39%||<0.05|
|Government employee||7, 1.97%||6, 85.71%||0||1, 14.29%|
|Private employer/Business||217, 61.13%||169, 77.88%||37, 17.05%||11, 5.07%|
|Unemployed||131, 36.90%||35, 26.72%||68, 51.91%||28, 21.37%||<0.05|
|Rs.5001 or more||56, 15.77%||50, 89.29%||2, 3.57%||4, 7.14%|
|<Rs.5000||202, 56.90%||133, 65.84%||34, 16.83%||35, 17.33%|
|No income||97, 27.32%||27, 27.84%||69, 71.13%||1, 1.03%||<0.05|
|Distance to HIV care facility from home|
|0-10 km||159, 44.79%||89, 55.97%||41, 25.79%||29, 18.24%|
|11-20 km||126, 35.49%||80, 63.49%||39, 30.95%||7, 5.56%|
|20+ km||70, 19.72%||41, 58.57%||25, 35.71%||4, 5.71%||<0.05|
Table 2: Gender stratified socio-demographic profile of adult PLHIVs in Delhi-NCR, Jul 2011 (N=355).
HIV/AIDS healthcare services that were minimally utilized by PLHIVs were psycho-social counseling and treatment of any STI. Utilization of majority of HIV/AIDS healthcare services varied by gender (p<0.05) as can be seen in Table 3. Substantial number of ‘other’ and male PLHIVs utilized pre-test and post-test counseling. Lesser proportion of female PLHIVs utilized pre-test and post-test counseling compared to male and ‘other’ PLHIVs (p<0.05). Greater proportion of female PLHIVs did not seek treatment for any OI and STI and similarly greater proportion of male PLHIVs did not seek treatment for any STI (p<0.05). Utilization of free condoms was optimum among ‘other’ PLHIVs but lesser among female PLHIVs (p<0.05) (Table 3).
|Yes||284, 80.00%||167, 58.80%||79, 27.82%||38, 13.38%||<0.05|
|No||71, 20.00%||43, 60.56%||26, 36.62%||2, 2.82%|
|Yes||295, 83.10%||175, 59.32%||80, 27.12%||40, 13.56%||<0.05|
|No||60, 16.90%||35, 58.33%||25, 41.67%||0, 0.00%|
|Yes||342, 96.34%||202, 59.06%||102, 29.82%||38, 11.11%||0.81|
|No||13, 3.66%||8, 61.54%||3, 23.08%||2, 15.38%|
|Yes||34, 9.58%||20, 58.82%||13, 38.24%||1, 2.94%||0.19|
|No||321, 90.42%||190, 59.19%||92, 28.66%||39, 12.15%|
|Positive living counseling|
|Yes||326, 91.83%||196, 60.12%||93, 28.53%||37, 11.35%||0.34|
|No||29, 8.17%||14, 48.28%||12, 41.38%||3, 10.34%|
|Treatment for any OI1|
|Yes||190, 57.40%||130, 68.42%||42, 22.11%||18, 9.47%||<0.05|
|No||141, 42.60%||62, 43.97%||59, 41.84%||20, 14.18%|
|Treatment for any STI2|
|Yes||123, 34.94%||60, 48.78%||41, 33.33%||22, 17.89%||<0.05|
|No||229, 65.06%||148, 64.63%||63, 27.51%||18, 7.86%|
|Yes||250, 70.42%||156, 62.40%||59, 23.60%||35, 14.00%||<0.05|
|No||105, 29.58%||54, 51.43%||46, 43.81%||5, 4.76%|
Table 3: Gender stratified utilization of HIV/AIDS healthcare services by adult PLHIVs in Delhi-NCR, Jul 2011 (N=355).
Both male (OR=0.20, 95% CI: 0.47, 0.88, p<0.05) and female (OR=0.16, 95% CI: 0.03, 0.70, p<0.05) PLHIVs were less likely to use pre-test counseling than ‘other’ PLHIVs. Male PLHIVs better utilized treatment for any OI than ‘other’ PLHIVs. Male PLHIVs were 3 times (ORa=3.29, 95% CI: 1.27, 8.51, p<0.05) more likely to get treatment for OI than ‘other’ PLHIVs after socio-demographic factors were adjusted for. Contrary to seeking treatment for OI, male PLHIVs were less likely to seek treatment for STIs. Male PLHIVs were 0.30 (95% CI: 0.12, 0.73, p<0.05) times less likely to utilize treatment for STI than ‘other’ PLHIV after adjusting for socio-demographic factors. Both male and female PLHIVs were less likely to utilize free condoms than ‘other’ PLHIVs. After confounders were adjusted, male PLHIVs were 0.24 (95% CI: 0.07, 0.80, p<0.05) times and female PLHIVs were 0.06 (95% CI: 0.01, 0.25, p<0.05) times less likely to utilize free condoms than ‘other’ PLHIVs. Due to zero cell count for ‘post-test counseling’, logistic regression model did not converge (Table 4).
OR (95%CI) p-value
OR (95%CI) p-value
|Crude||0.20 (0.47, 0.88) <0.05||0.16 (0.03, 0.70) <0.05||Ref|
|Adjusted||0.18 (0.03, 0.96) <0.05||0.21 (0.03, 1.21) 0.08||Ref|
|Treatment for any OI2|
|Crude||2.33 (1.15, 4.71) <0.05||0.79 (0.37, 1.67) 0.54||Ref|
|Adjusted||3.29 (1.27, 8.51) <0.05||1.06 (0.36, 3.10) 0.90||Ref|
|Treatment for any STI|
|Crude||0.33 (0.16, 0.66) <0.05||0.53 (0.25, 1.11) 0.09||Ref|
|Adjusted||0.30 (0.12, 0.73) <0.05||0.39 (0.14, 1.09) 0.07||Ref|
|Crude||0.41 (0.15, 1.10) 0.07||0.18 (0.06, 0.50) <0.05||Ref|
|Adjusted||0.24 (0.07, 0.80) <0.05||0.06 (0.01, 0.25) <0.05||Ref|
Table 4: Crude and adjusted1 Odds Ratios (OR) indicating gender stratified utilization of HIV/AIDS healthcare services by adult PLHIVs in Delhi-NCR, Jul 2011 (N=355).
Targeted intervention impairs optimum and equal utilization of HIV/AIDS healthcare services across genders. Common barriers to HIV healthcare services utilization such as age, number of years since HIV +ve, marital status, children, religion, caste/tribe affiliation, education, employment, monthly income, and distance to HIV care facility from home were modeled in multivariate analysis and gender continued to be a significant determinant. Utilization significantly varied by gender among adult PLHIVs of Delhi-NCR. Almost all services were less utilized by those PLHIVs who belonged to the conventional malefemale gender dyad which is not a priority of targeted intervention strategy.
This study of PLHIVs of Delhi-NCR divulges disparities in HIV/ AIDS healthcare services utilization when targeted intervention strategy is being implemented. Patterns in utilization of HIV/AIDS healthcare services are evident. We found that despite HIV/AIDS healthcare services utilization data being collected at points of service delivery, utilization was not optimum. Moreover, utilization varied by gender. Interesting association between gender and utilization of HIV/AIDS healthcare services became apparent. Male PLHIVs were less likely to utilize pre-test counseling, treatment for any STI, and free condoms than ‘other’ PLHIVs. These findings are consistent with what is known about healthcare seeking behavior of men in India [34-36,42].
It is also known that due to the stigma attached with sex and HIV, HIV programs have had limited success [10,15,18-20,38,39]. Probably targeted approach reinforces already existing stigma [10,15,18,19]. Through targeted interventions ‘other’ PLHIVs are being aggressively reached-out but those (male and female) belonging to the so called “general population” are among the underserved/un-served. There is substantial evidence that all population groups need to be cateredto since sexual transmission of HIV is not only heterosexual but also homosexual and bisexual and since “general population” serves as a “bridge group” [2,6-10,13]. Women in India, are not only targeted by reproductive and sexual health programs they also happen to receive some sexual/reproductive healthcare during maternity [10,15,30-34,36,37]. With respect to HIV/AIDS, healthcare utilization by women is hindered due to their inferior status in the society, passive healthcare seeking behavior, socioeconomic factors, and stigma around sex and HIV [10,15,18,19,37-41].
Interestingly, of all the HIV/AIDS healthcare services that we analyzed the data for, only treatment of any OI was better sought by male PLHIVs than ‘other’ PLHIVs. Health studies in the past have indicated that men compared to women enjoy the privilege of seeking treatment and cure for common illnesses/infections due to their favorable socioeconomic position in the society [33,35,36]. Our study findings were consistent with this conventional trend in the country. We speculate that since treatment for OIs can be sought at facilities which are visited by both HIV +ve and HIV -ve people, fear of stigma does not prevent men from utilizing this service.
Reproductive and sexual health programs in the country in the past have highlighted deficiencies of targeted approach [30-33]. Results of our study show gender disparities that targeted intervention strategy can yield. To contain the spread of the [HIV] virus and to ensure good health it is pertinent that healthcare is available to all, irrespective of their gender identity and presumed indulgence in risk behaviors [10,15,20,43,44,47,52,53].
To our knowledge, this is the first study where gender disparities in HIV/AIDS healthcare services utilization are examined across 3 gender groups in context of targeted intervention and alternative gender identities that are characteristic of our country. This study is well timed when the country is embarking upon universal healthcare and when unconventional/alternative gender identities are slowly gaining recognition [28,54-56]. Health professionals have expressed a need for gender stratified analysis of HIV/AIDS data to guide strategies for universal health coverage [45,47,54]. More such studies will not only strengthen the knowledge-base but will also provide useful directions. Probably, qualitative population-based and hospital-based studies with PLHIVs will reveal more about success/failures of targeted and non-targeted healthcare programs. We suspect that similar pattern of healthcare services utilization- ‘other’ utilizing more than male or female – will be evident if other dimensions of reproductive and sexual health were being studied. Future research related to gender disparities in healthcare need to closely examine such phenomenon.
Our study had a few limitations non-probability convenience sample had to be recruited due to which external validity is restricted to urban PLHIVs who have apparently initiated services utilization. Since study population belongs to a stigmatized group of the society it was difficult to find eligible sample willing to participate in the study. Given this, we had to group all alternative gender/sexual identities into one category: ‘other’. Gender stratified HIV/AIDS healthcare services utilization data and studies were sparingly available so study findings had to be compared with reproductive and sexual health studies that had male-female segregated data. Regardless of these limitations findings of this study are intuitive. A need for a strategy to ensure that healthcare services are equally utilized by all irrespective of their gender affiliation and presumed engagement in risk-taking behavior is established. Targeting interventions pose a challenge to comprehensive and universal health coverage. NACP III is designed to ensure universal access to quality HIV care and to minimize associated gender disparities . Given the unique cultural context of our country, transformative strategies for gender equity are need of the hour [47,48,52]. Achieving gender equity in HIV care indicates effectiveness in technical management of HIV disease as well as integration of gender into overall health and development efforts [43,47-49,52,57].
Co-author, Ms. Qureshi, assisted with literature review, preliminary analysis, and writing of the manuscript. We acknowledge support of Saksham, GFATMr-7 HIV/AIDS Counseling Program at Tata Institute of Social Sciences, Mumbai and Jamia Millia Islamia, New Delhi, and Nai Umang Positive Welfare Society, New Delhi, India.
None declared. This study was supported by Saksham, GFATMr-7 HIV/AIDS Counseling Program, Tata Institute of Social Sciences; Mumbai, India provided financial support to Saksham, Jamia Millia Islamia, New Delhi, India.