AIDS Care, 2015 Vol. 27, No. 7, 921–925, http://dx.doi.org/10.1080/09540121.2015.1015483
Using a call center to encourage linkage to care following mobile HIV counseling and testing Michiel Adriaan van Zyla*, Leslie Lauren Browna and Kathryn Pahlb a
Kent School of Social Work, University of Louisville, Louisville, KY, USA; bShout-It-Now, Cape Town, South Africa
(Received 26 September 2014; accepted 29 January 2015) Engaging newly diagnosed HIV+ individuals in treatment is a significant global challenge. As South Africa expands HIV counseling and testing (HCT) services, the growing numbers of people diagnosed with HIV will need innovative links to care approaches in order for treatment to be most effective. While definitions vary, we have defined “linkage to care” as connecting an HIV+ individual to medical care, so that CD4 cell test results are obtained and antiretroviral therapy (ART) eligibility assessed. The study is of HIV+ participants (n = 1096), from either Limpopo or Gauteng provinces from a “Links to Care” program. A two-pronged expanded HCT service was used, which included a community outreach approach to address HIV testing and a call center to encourage and track each patient’s linkage to care post-HIV diagnosis. The majority of individuals (51%) were linked to care with a mean time to linkage of 31 days (with most individuals linked in less than 14 days). More females (54%) were linked to care than males (47%) and had higher CD4 cell counts than males; females had a mean CD4 cell count of 440, while males took longer to link to care and had a lower mean CD4 cell count of 331. Females of 23 years or younger had the lowest linkage rate of all females. Findings suggest that expanding HCT services to include innovative links to care approaches can improve linkage to care and subsequently impact HIV prevention. Keywords: HIV/AIDS; South Africa; HCT; linkage to care; ART
Introduction Although antiretroviral therapy (ART) availability is integral to HIV treatment (Andrews, Wood, Bekker, Middlekoop, & Walensky, 2012; Crum et al., 2006; Giordano et al., 2007; Mayer, 2011), the effectiveness of ART can be contingent upon the timeliness of linkage to care (Lawn, Harries, Anglaret, Myer, & Wood, 2008; Losina et al., 2010), underscoring the need to improve services that impact linkage to care (Jenness et al., 2012; Mayer, 2011) as a means of improving overall survival rate (Bogart et al., 2013). Given the dearth of information on loss to follow up between HIV diagnosis and the preART stage (Bogart et al., 2013), there is a need to improve linkage to care in low-resource countries (Mugglin et al., 2012). Study questions, on trends associated with linkage to care using mobile HIV counseling and testing (HCT) combined with a call center approach, were as follows: (1) what percentage of HIV+ individuals were reached, and how does the demographic data vary for those linked to care and those not linked to care? (2) How long does it take to link clients to care and are rates similar or different for various demographic groups?
clients’ preferred language were made to those recently testing HIV+ during HCT (see Van Zyl, Barney, & Pahl, 2014, for a description of the approach). The sample included all individuals who tested HIV+ during mobile HCT outreach in Limpopo (rural area) and Gauteng (urban area) provinces in South Africa between 1 April 2012 and 31 October 2012 (n = 1096). “Linkage to care” was defined as connecting HIV+ individuals to medical care, so that CD4 cell test results are obtained and ART eligibility assessed. Administrative data collected as part of rendering of HCT (de-identified in accordance with standard 45 CFR §164.514(a)(b)) were provided for analysis. The institutional review board at the University of Louisville, Kentucky, USA, reviewed this study and determined that it was exempt from the HHS regulations for the protection of human subjects. Descriptive statistics were used to describe the sample, and differences between various cohorts were detected by means of t-tests and CHAID (Chi-squared Automatic Interaction Detector) analysis (e.g., age, gender, and language). CHAID is, in essence, a tree classification method that relies on the chi-square test to determine the best next split at each step of the classification tree.
Methods This secondary data analysis reviews data collected during seven months of operation of an HIV linkage to care call center where daily follow-up telephone calls in *Corresponding author. Email: [email protected] © 2015 Taylor & Francis
Results The majority of individuals (68.7%) referred to the call center during the study period were female and 31.3%
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Figure 1. CHAID analysis of significant differences in linkage status (“linked to care” vs “not linked to care”) in age groups and gender of HIV+ patients and the call center team responsible for linking them to care. Note: “Missing” in Figure 1 refers to cases with missingness in data. At the first child node level, missing cases have similar linkage status than the 33–34 age group, and they are therefore combined with this age group in a box in the diagram. The “ –” symbol at the bottom of a box in the diagram means that the node further divides into a child node.
were male. The mean age was 32.82 years (SD = 10.29) with males having a higher mean age (M = 35.82; SD = 10.9) than that of females (M = 31.46; SD = 9.71; t(1092) = 6.626; p < .001). A CHAID analysis was conducted to identify significant differences in linkage rates among age, gender, and urban/rural cohorts (Figure 1). Linkage to care was established for 563 (51.4%) of HIV+ individuals referred to the call center, and 95.9% of those, for whom linkages were established, were newly diagnosed as HIV+. The mean age (M = 31.1; SD = 9.96) of individuals linked to care was similar (M = 32.52; SD = 10.63) to those not linked to care (t(1092) = .936; p = .339). This
was true for both males (linked M = 36.5; SD = 11.43; not linked M = 35.24; SD = 10.41; t(340) = 1.059; p = .29) and females (linked M = 31.77; SD = 8.98; not linked M = 31.1; SD = 10.48; t(750) = .938; p = .349). More females (53.5%) than males (46.61%; χ2 = .037; df = 1) were linked to care. For females, age contributed to differences in linkage status. A relatively high percentage of people in the age group 33–43 was linked to care (61.8%), which was also true for people for whom age was unknown. Females under 23 was the female age group with the lowest percentage linked to care (41.8%). For this group, the rural team linked more (52.3%) young females
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Figure 2. CHAID analysis of significant differences in reasons for not being linked to care between age groups and gender of HIV+ patients and the call center team responsible for linking them to care. Note: Significant differences in reasons were only identified between the two geographical areas serviced by the two teams. Reasons did not differ across age groups or gender. Also the most prominent difference was for the category “Called many times.”
to care than the urban team (32.9%). The mean CD4 count was 399.19 (SD = 244.44) and the median 370. For males, the CD4 mean was 331.06 (SD = 205.53), while for females it was 439.72 (SD = 231.75; t(548) = 5.107; p < .001), indicating that males require on average more immediate ART intervention than females. Reasons for not being linked to care within 30 days of referral to the call center (n = 442) were analyzed (see Figure 2). Most (56.3%) were contacted many times without following through with a visit to a clinic. A total of 17.9% provided incorrect contact information. Just over fourteen percent (14.5%) asked not to be called and 11.3% had no telephone. The urban team made a higher percentage of repeated attempts to contact individuals without success (64.7%) than the rural team (44.9%), and the rural team had a higher percentage of individuals referred to them without a telephone (20.3% compared to 4.7% with the urban team). Discussion Results compared favorably with previous studies on mobile HCT, but variations in definitions of linkage to care obfuscate data comparisons. Bassett,
Regan, et al. (2014) reported only 10% linkage to care for mobile HCT testers within three months; a comparable sample found linkage rates similar to our finding (53.4%; Krazner et al., 2010), and results from Durban, South Africa, showed a 47% linkage to CD4 cell results (Losina et al., 2010). Numerous studies based their follow-up times on CD4 stratums (lower counts receiving fastest follow-up, etc.). For example, Krazner et al. (2012) reported a linkage rate of 78% among mobile HCT testers near Cape Town, but linkage follow-up occurred at 4 and 12 weeks respective to CD4 cell stratum, and there was a much smaller sample size. Govindassamy et al. (2013) also tracked linkage rates by CD4 stratum, finding in the Western Cape, South Africa, that at one month only 37% of individuals had linked to care. Delaying entry to care has been associated with lower CD4 cell counts (Mugglin et al., 2012; Patten et al., 2013) and subsequently hastened mortality (Kayigamba, Bakker, Fikse, Mugisha, & Asiimwe, 2012; Larson et al., 2010); yet mobile testing has been shown to detect HIV at earlier stages of the disease cycle, as evidenced by higher CD4 counts (Bassett, Govindassamy, et al., 2014). Our mobile HCT study appears to
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have procured CD4 counts that were comparably high. Mugglin et al.’s (2012) meta-analysis found that among six studies median CD4 counts ranged between 154 and 274, and Govender et al. (2014) found a median CD4 count of 364, both slightly lower than our median count of 370. Gender differences in our study were consistent with several sources reporting on HCT (Govender et al., 2014; Govindassamy et al., 2013; May et al., 2010; Patten et al., 2013); most individuals referred to the call center were female (similar to Govindassamy et al., 2013), whom were more likely to link to care than males and had higher CD4 counts. Govindassamy et al. (2013) reported a similar mean CD4 count (481) among women. Bassett, Govindassamy, et al. (2014) found that linkage is stratified by gender (males at 31% and females at 51%); linkage rates among our sample were higher for both gender, but our study had a smaller disparity between male and female linkage. Despite females, in general, having higher linkage rates, young females (under 23 years of age) had the lowest linkage rate; this is also consistent with the literature. Youth are more likely to be lost to follow up between HCT and ART initiation (Mugglin et al., 2012; Patten et al., 2013), and young women (aged 15–24), specifically, experience a host of difficulties that cause them to be vulnerable (UNAIDS, 2013). There are several limitations to this study. First, the study does not include a comparison of linkage rates before and after the call center approach was implemented. The study design does not include a comparison with other approaches to linking HIV+ individuals to care. Second, the study included only two provinces of South Africa. Findings cannot be generalized to other regions with different demographic compositions and infection incidence. Third, the study involved individuals in a seven-month time frame. Fourth, limited information was obtained for the group not linked to care. Finally, this study only focuses on linkage to care and does not include a focus on retention in care. The HIV treatment cascade (Rosen & Fox, 2011; Valdiserri, 2012) demonstrates that more attention is needed at all stages of the continuum of care, especially in sub-Saharan Africa (Elul et al., 2014). This study also had strengths. The sample size (n = 1096), although not large, is larger than other HIV linkage to care studies that tracked individuals for a comparable length of time: Losina et al. (2010; N = 454) and Van Rooyen et al. (2013; N = 201). The sample we studied had the added advantage of diversity, as participants were from both urban and rural areas. Study findings offer useful insights about trends associated with linkage to care as well as delayed entry to care. Future studies should focus also on factors associated with delayed entry to care (Reed et al., 2009)
and HIV+ individuals with CD4 counts of ≤350, who seem to take longer to link than individuals with a higher count. As definitions of linkage to care vary, future studies would benefit from the development of a standardized definition. Disclosure statement No potential conflict of interest was reported by the authors.
Funding The call center and collection of administrative data for this study were supported by the Center for Disease Control through [grant number 1U2GGH000285-03].
References Andrews, J. R., Wood, R., Bekker, L.-G., Middlekoop, K., Walensky, R. P. 2012. Projecting the benefits of antiretroviral therapy for HIV prevention: The impact of population mobility and linkage to care. The Journal of Infectious Diseases, 206, 543–551. doi:10.1093/infdis/jis401 Bassett, I. V., Govindassamy, D., Erlwanger, A. S., Hyle, E. P., Krazner, K., Van Schaik, N., … Freedberg, K. A. (2014). Mobile HIV screening in Cape Town, South Africa: Clinical impact, cost, and cost-effectiveness. PLoS One, 9(1). doi:10.1371/journal.pone.0085197.s003 Bassett, I. V., Regan, S., Luthuli, P., Mbonambi, H., Bearnot, B., Pendleton, A., … Mhlongo, B. (2014). Linkage to care following community-based mobile HIV testing compared with clinic based testing in Umlazi Township, Durban, South Africa. HIV Medicine, 15, 367–372. doi:10.1111/ hiv.12115 Bogart, L. M., Chetty, S., Giddy, J., Sypek, A., Sticklor, L., Walensky, R. P., … Bassett, I. V. (2013). Barriers to care among people living with HIV in South Africa: Contrasts between patient and healthcare provider perspectives. AIDS Care, 25, 843–853. doi:10.1080/09540121.2012.729808 Crum, N. F., Riffenburgh, R. H., Wegner, S., Agan, B. K., Tasker, S. A., Spooner, K. M., … Wallace, M. R. (2006). Comparisons of causes of death and mortality rates among HIV-infected persons: Analysis of the pre-, early, and late HAART (highly active antiretroviral therapy) eras. Journal of Acquired Immune Deficiency Syndromes, 41, 194– 200. doi:10.1097/01.qai.0000179459.31562.16 Elul, B., Lahuerta, M., Abacassamo, F., Lamb, M. R., Ahoua, L., McNairy, M. L., … Jani, I. (2014). A combination strategy for enhancing linkage to care and retention in HIV care among adults newly diagnosed with HIV in Mozambique: Study protocol for a site-randomized implementation science study. BMC Infectious Diseases, 14, 549. doi:10.1186/s12879-014-0549-5 Giordano, T. P., Gifford, A. L., White, A. C., Jr., SuarezAlmazor, M. E., Rabeneck, L., Hartman, C., … Morgan, R. O. (2007). Retention in care: A challenge to survival with HIV infection. Clinical Infectious Diseases, 44, 1493–1499. doi:10.1086/516778 Govender, S., Otwombe, K., Essien, T., Panchia, R., de Bruyn, G., Mohapi, L., … Martinson, N. (2014). CD4 counts and viral loads of newly diagnosed HIV-infected individuals:
AIDS Care Implications for treatment as prevention. PLoS One, 9(3), 1–6. Govindassamy, D., Krazner, K., van Schaik, N., Noubary, F., Wood, R., Walensky, R. P., … Bekker, L. G. (2013). Linkage to HIV, TB and Non communicable disease care from a mobile testing unit in Cape Town, South Africa. PLoS One, 8, e80017. doi:10.1371/journal.pone.0080017. s004 Jenness, S. M., Myers, J. E., Neaigus, A., Lulek, J., Navejas, M., & Raj-Singh, S. (2012). Delayed entry into HIV medical care after HIV diagnosis: Risk factors and research methods. AIDS Care: Psychological and Socio Medical Aspects of AIDS/HIV, 24, 1240–1248. doi:10.1080/09540 121.2012.656569 Kayigamba F. R., Bakker, M. I., Fikse, H., Mugisha, V., & Asiimwe, A. (2012). Patient enrolment into HIV care and treatment within 90 days of HIV diagnosis in eight Rwandan health facilities: A review of facility-based registers. PLoS One, 7(5), 1–9. Krazner, K., Govindassamy, D., van Schaik, N., Thebus, E., Davies, N., Zimmermann, M. A., … Bekker, L.-G. (2012). Incentivized recruitment of a population sample to a mobile HIV testing service increases the yield of newly diagnosed cases, including those in need of antiretroviral therapy. HIV Medicine, 13, 132–137. doi:10.1111/j.14681293.2011.00947.x Krazner, K., Zeinecker, J., Ginsbert, P., Orrell, C., Kalawe, N., Lawn, S., … Wood, R. (2010). Linkage to HIV care and antiretroviral therapy in Cape Town, South Africa. PLoS One, 5, 1–6. Larson, B. A., Brennan, A., McNamara, L., Long, L., Rosen, S., Sanne, I., & Fox, M. (2010). Lost opportunities to complete CD4+ Lymphocyte Testing among patients who tested positive for HIV in South Africa. Bull World Health Organ, 88, 675–680. doi:10.2471/BLT.09.068981 Lawn, S. D., Harries, A. D., Anglaret, X., Myer, L., & Wood, R. (2008). Early mortality among adults accessing antiretroviral treatment programmes in sub-Saharan Africa. AIDS, 22, 1897–1908. doi:10.1097/QAD.0b013e32830007cd Losina, E., Bassett, I., Giddy, J., Chetty, S., Regan, S., Walensky, R. P., … Freedberg, K. A. (2010). The “ART” of linkage: Pre-treatment loss to are after HIV diagnosis at two PEPFAR sites in Durban, South Africa. PLoS One, 5(3), 1–8. May, M., Boulle, A., Phiri, S., Messou, E., Myer, L., Wood, R., … Egger, M. (2010). Prognosis of patients with HIV-1 infection starting antiretroviral therapy in sub-Saharan Africa: A
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collaborative analysis of scale-up programmes. Lancet, 376, 449–457. doi:10.1016/S0140-6736(10)60666-6 Mayer, K. H. (2011). Introduction: Linkage, engagement, and retention in HIV care: Essential for optimal individualand community-level outcomes in the Era of highly active antiretroviral therapy. Clinical Infectious Diseases, 52(Suppl. 2), 205–207. Mugglin, C., Estill, J., Wandeler, G., Bender, N., Egger, M., Gsponer, T., & Keiser, O. (2012). Loss to programme between HIV diagnosis and initiation of antiretroviral therapy in sub- Saharan Africa: Systematic review and meta-analysis. Tropical Medicine and International Health, 17, 1509–1520. doi:10.1111/j.1365-3156.2012.03089.x Patten, G. E. M., Wilkinson, L., Conradie, K., Isaakidis, P., Harries, A. D., Edginton, M. E., … van Cutsem, G. (2013). Impact on ART initiation of point-of-care CD4 cell testing at HIV diagnosis among HIV-positive youth in Khayelitsha, South Africa. Journal of the International AIDS Society, 16, 18518. doi:10.7448/IAS.16.1.18518 Reed, J. B., Hanson, D., McNaghten, A. D., Bertolli, J., Teshale, E., Gardner, L., & Sullivan, P. (2009). HIV testing factors associated with delayed entry into HIV medical care among HIV-infected persons from eighteen states, United States, 2000–2004. AIDS Patient Care STDS, 23, 765–773. Rosen, S., & Fox, M. P. (2011). Retention in HIV care between testing and treatment in sub Saharan Africa: A systematic review. PLoS Medicine, 8, e1001056. doi:10.1371/journal. pmed.1001056.s002 UNAIDS. (2013). Global Report: UNAIDS Report on the Global AIDS Epidemic 2013. Geneva: Author. Valdiserri, R. (2012). HIV/AIDS Treatment Cascade Helps Identify Gaps in Care, Retention. AIDS.gov. blog.AIDS. gov. Retrieved June 14, 2014, from http://blog.aids.gov/ 2012/07/hivaids-treatment-cascade-helps-identifygaps-incareretention.html Van Rooyen, H., Barnabas, R. V., Baeten, J. M., Phakathi, Z., Joseph, P., Krows, M., … Celum, C. (2013). High HIV testing uptake and linkage to care in a novel program of home-based HIV counseling and testing with facilitated referral in KwaZulu-Natal, South Africa. Journal of Acquired Immune Deficiency Syndrome, 64(1), e–e8. Van Zyl, M. A., Barney, R. J., & Pahl, K. (2014). Voluntary counseling and testing and celebrity-based HIV/AIDS prevention education: A pilot program implemented in Cape Town Secondary Schools. Journal of HIV AIDS Social Services, 13, 292–305. doi:10.1080/15381501.2013.864174
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Using a call center to encourage linkage to care following mobile HIV counseling and testing Michiel Adriaan van Zyla*, Leslie Lauren Browna and Kathryn Pahlb a
Kent School of Social Work, University of Louisville, Louisville, KY, USA; bShout-It-Now, Cape Town, South Africa
(Received 26 September 2014; accepted 29 January 2015) Engaging newly diagnosed HIV+ individuals in treatment is a significant global challenge. As South Africa expands HIV counseling and testing (HCT) services, the growing numbers of people diagnosed with HIV will need innovative links to care approaches in order for treatment to be most effective. While definitions vary, we have defined “linkage to care” as connecting an HIV+ individual to medical care, so that CD4 cell test results are obtained and antiretroviral therapy (ART) eligibility assessed. The study is of HIV+ participants (n = 1096), from either Limpopo or Gauteng provinces from a “Links to Care” program. A two-pronged expanded HCT service was used, which included a community outreach approach to address HIV testing and a call center to encourage and track each patient’s linkage to care post-HIV diagnosis. The majority of individuals (51%) were linked to care with a mean time to linkage of 31 days (with most individuals linked in less than 14 days). More females (54%) were linked to care than males (47%) and had higher CD4 cell counts than males; females had a mean CD4 cell count of 440, while males took longer to link to care and had a lower mean CD4 cell count of 331. Females of 23 years or younger had the lowest linkage rate of all females. Findings suggest that expanding HCT services to include innovative links to care approaches can improve linkage to care and subsequently impact HIV prevention. Keywords: HIV/AIDS; South Africa; HCT; linkage to care; ART
Introduction Although antiretroviral therapy (ART) availability is integral to HIV treatment (Andrews, Wood, Bekker, Middlekoop, & Walensky, 2012; Crum et al., 2006; Giordano et al., 2007; Mayer, 2011), the effectiveness of ART can be contingent upon the timeliness of linkage to care (Lawn, Harries, Anglaret, Myer, & Wood, 2008; Losina et al., 2010), underscoring the need to improve services that impact linkage to care (Jenness et al., 2012; Mayer, 2011) as a means of improving overall survival rate (Bogart et al., 2013). Given the dearth of information on loss to follow up between HIV diagnosis and the preART stage (Bogart et al., 2013), there is a need to improve linkage to care in low-resource countries (Mugglin et al., 2012). Study questions, on trends associated with linkage to care using mobile HIV counseling and testing (HCT) combined with a call center approach, were as follows: (1) what percentage of HIV+ individuals were reached, and how does the demographic data vary for those linked to care and those not linked to care? (2) How long does it take to link clients to care and are rates similar or different for various demographic groups?
clients’ preferred language were made to those recently testing HIV+ during HCT (see Van Zyl, Barney, & Pahl, 2014, for a description of the approach). The sample included all individuals who tested HIV+ during mobile HCT outreach in Limpopo (rural area) and Gauteng (urban area) provinces in South Africa between 1 April 2012 and 31 October 2012 (n = 1096). “Linkage to care” was defined as connecting HIV+ individuals to medical care, so that CD4 cell test results are obtained and ART eligibility assessed. Administrative data collected as part of rendering of HCT (de-identified in accordance with standard 45 CFR §164.514(a)(b)) were provided for analysis. The institutional review board at the University of Louisville, Kentucky, USA, reviewed this study and determined that it was exempt from the HHS regulations for the protection of human subjects. Descriptive statistics were used to describe the sample, and differences between various cohorts were detected by means of t-tests and CHAID (Chi-squared Automatic Interaction Detector) analysis (e.g., age, gender, and language). CHAID is, in essence, a tree classification method that relies on the chi-square test to determine the best next split at each step of the classification tree.
Methods This secondary data analysis reviews data collected during seven months of operation of an HIV linkage to care call center where daily follow-up telephone calls in *Corresponding author. Email: [email protected] © 2015 Taylor & Francis
Results The majority of individuals (68.7%) referred to the call center during the study period were female and 31.3%
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Figure 1. CHAID analysis of significant differences in linkage status (“linked to care” vs “not linked to care”) in age groups and gender of HIV+ patients and the call center team responsible for linking them to care. Note: “Missing” in Figure 1 refers to cases with missingness in data. At the first child node level, missing cases have similar linkage status than the 33–34 age group, and they are therefore combined with this age group in a box in the diagram. The “ –” symbol at the bottom of a box in the diagram means that the node further divides into a child node.
were male. The mean age was 32.82 years (SD = 10.29) with males having a higher mean age (M = 35.82; SD = 10.9) than that of females (M = 31.46; SD = 9.71; t(1092) = 6.626; p < .001). A CHAID analysis was conducted to identify significant differences in linkage rates among age, gender, and urban/rural cohorts (Figure 1). Linkage to care was established for 563 (51.4%) of HIV+ individuals referred to the call center, and 95.9% of those, for whom linkages were established, were newly diagnosed as HIV+. The mean age (M = 31.1; SD = 9.96) of individuals linked to care was similar (M = 32.52; SD = 10.63) to those not linked to care (t(1092) = .936; p = .339). This
was true for both males (linked M = 36.5; SD = 11.43; not linked M = 35.24; SD = 10.41; t(340) = 1.059; p = .29) and females (linked M = 31.77; SD = 8.98; not linked M = 31.1; SD = 10.48; t(750) = .938; p = .349). More females (53.5%) than males (46.61%; χ2 = .037; df = 1) were linked to care. For females, age contributed to differences in linkage status. A relatively high percentage of people in the age group 33–43 was linked to care (61.8%), which was also true for people for whom age was unknown. Females under 23 was the female age group with the lowest percentage linked to care (41.8%). For this group, the rural team linked more (52.3%) young females
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Figure 2. CHAID analysis of significant differences in reasons for not being linked to care between age groups and gender of HIV+ patients and the call center team responsible for linking them to care. Note: Significant differences in reasons were only identified between the two geographical areas serviced by the two teams. Reasons did not differ across age groups or gender. Also the most prominent difference was for the category “Called many times.”
to care than the urban team (32.9%). The mean CD4 count was 399.19 (SD = 244.44) and the median 370. For males, the CD4 mean was 331.06 (SD = 205.53), while for females it was 439.72 (SD = 231.75; t(548) = 5.107; p < .001), indicating that males require on average more immediate ART intervention than females. Reasons for not being linked to care within 30 days of referral to the call center (n = 442) were analyzed (see Figure 2). Most (56.3%) were contacted many times without following through with a visit to a clinic. A total of 17.9% provided incorrect contact information. Just over fourteen percent (14.5%) asked not to be called and 11.3% had no telephone. The urban team made a higher percentage of repeated attempts to contact individuals without success (64.7%) than the rural team (44.9%), and the rural team had a higher percentage of individuals referred to them without a telephone (20.3% compared to 4.7% with the urban team). Discussion Results compared favorably with previous studies on mobile HCT, but variations in definitions of linkage to care obfuscate data comparisons. Bassett,
Regan, et al. (2014) reported only 10% linkage to care for mobile HCT testers within three months; a comparable sample found linkage rates similar to our finding (53.4%; Krazner et al., 2010), and results from Durban, South Africa, showed a 47% linkage to CD4 cell results (Losina et al., 2010). Numerous studies based their follow-up times on CD4 stratums (lower counts receiving fastest follow-up, etc.). For example, Krazner et al. (2012) reported a linkage rate of 78% among mobile HCT testers near Cape Town, but linkage follow-up occurred at 4 and 12 weeks respective to CD4 cell stratum, and there was a much smaller sample size. Govindassamy et al. (2013) also tracked linkage rates by CD4 stratum, finding in the Western Cape, South Africa, that at one month only 37% of individuals had linked to care. Delaying entry to care has been associated with lower CD4 cell counts (Mugglin et al., 2012; Patten et al., 2013) and subsequently hastened mortality (Kayigamba, Bakker, Fikse, Mugisha, & Asiimwe, 2012; Larson et al., 2010); yet mobile testing has been shown to detect HIV at earlier stages of the disease cycle, as evidenced by higher CD4 counts (Bassett, Govindassamy, et al., 2014). Our mobile HCT study appears to
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have procured CD4 counts that were comparably high. Mugglin et al.’s (2012) meta-analysis found that among six studies median CD4 counts ranged between 154 and 274, and Govender et al. (2014) found a median CD4 count of 364, both slightly lower than our median count of 370. Gender differences in our study were consistent with several sources reporting on HCT (Govender et al., 2014; Govindassamy et al., 2013; May et al., 2010; Patten et al., 2013); most individuals referred to the call center were female (similar to Govindassamy et al., 2013), whom were more likely to link to care than males and had higher CD4 counts. Govindassamy et al. (2013) reported a similar mean CD4 count (481) among women. Bassett, Govindassamy, et al. (2014) found that linkage is stratified by gender (males at 31% and females at 51%); linkage rates among our sample were higher for both gender, but our study had a smaller disparity between male and female linkage. Despite females, in general, having higher linkage rates, young females (under 23 years of age) had the lowest linkage rate; this is also consistent with the literature. Youth are more likely to be lost to follow up between HCT and ART initiation (Mugglin et al., 2012; Patten et al., 2013), and young women (aged 15–24), specifically, experience a host of difficulties that cause them to be vulnerable (UNAIDS, 2013). There are several limitations to this study. First, the study does not include a comparison of linkage rates before and after the call center approach was implemented. The study design does not include a comparison with other approaches to linking HIV+ individuals to care. Second, the study included only two provinces of South Africa. Findings cannot be generalized to other regions with different demographic compositions and infection incidence. Third, the study involved individuals in a seven-month time frame. Fourth, limited information was obtained for the group not linked to care. Finally, this study only focuses on linkage to care and does not include a focus on retention in care. The HIV treatment cascade (Rosen & Fox, 2011; Valdiserri, 2012) demonstrates that more attention is needed at all stages of the continuum of care, especially in sub-Saharan Africa (Elul et al., 2014). This study also had strengths. The sample size (n = 1096), although not large, is larger than other HIV linkage to care studies that tracked individuals for a comparable length of time: Losina et al. (2010; N = 454) and Van Rooyen et al. (2013; N = 201). The sample we studied had the added advantage of diversity, as participants were from both urban and rural areas. Study findings offer useful insights about trends associated with linkage to care as well as delayed entry to care. Future studies should focus also on factors associated with delayed entry to care (Reed et al., 2009)
and HIV+ individuals with CD4 counts of ≤350, who seem to take longer to link than individuals with a higher count. As definitions of linkage to care vary, future studies would benefit from the development of a standardized definition. Disclosure statement No potential conflict of interest was reported by the authors.
Funding The call center and collection of administrative data for this study were supported by the Center for Disease Control through [grant number 1U2GGH000285-03].
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