by Michiel Adriaan van Zyl, Ph.D1, Leslie Lauren Brown, LCSW1, Kathryn Pahl, Ph.D2,
1Kent School of Social Work, University of Louisville, 502-852-2430 | 2Shout-It-Now, Cape Town, South Africa
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 ART eligibility assessed. The current study reveals findings from a non-governmental organization’s “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 centre to track each patient’s linkage to care post HIV diagnosis. In the evaluation sample (n=1096), all participants were diagnosed as HIV positive, from either Limpopo or Gauteng provinces, with 95.5% of individuals reportedly being newly diagnosed. 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 23 or younger had the lowest linkage rate of all females. Success rate differed by region, with 46.5% of individuals being linked to care in the rural area compared to 28% in the urban area. Reasons for the failure to link individuals to care are analyzed. Findings suggest that expanding HCT services to include innovative links to care approaches can improve linkage to care and subsequently impact HIV prevention.
Despite successful preventative efforts, South Africa continues to have the largest HIV/AIDS epidemic in the world (Mall, Middlekoop, Mark, Wood, & Bekker, 2013; van Rooyan, Barnabas, Baeten, Phakathi, Joseph, Krows, Hong, Murnane, Hughes, & Celum 2013). In order to maximize preventative efforts and curb transmission rates, more innovative and empirically sound interventions are needed. Research has shown HIV counseling and testing (HCT) to be instrumental in HIV prevention, yet very little literature has focused on ways to improve HCT and to link individuals to services after their HIV diagnosis. Mobile HCT enhances the traditional HIV testing model (typically only offered in health facilities) by providing community outreach and personalized HIV testing services; this approach aims to serve difficult-to-reach populations. Although antiretroviral therapy (ART) availability is integral to HIV treatment and prevention, the effectiveness of ART can be contingent upon the timeliness of linkage to care (Losina, Bassett, Giddy, Chetty, Regan, Walensky, Ross, Scott, Uhler, Kats, Holst, Freedberg, 2010), underscoring the need to improve services that impact linkage to care.
Preventative efforts are further confounded by the large number of individuals who are aware of their status but not engaged in care (De Koker, Lefevre, Matthys, van der Stuyft, & Delva, 2010; Van Zyl, Barney & Pahl, in Press). A recent study in a township in Durban, South Africa, found that 10% of participants in a community-based mobile testing linked to care after receiving an HIV diagnosis (Bassett et al, 2013). Because treatments such as ART rely on linkage to care, the effectiveness of HCT cannot be measured only by the numbers of those receiving HIV tests but rates of linkage to care should also be considered.
ART reduces morbidity by improving patient survival (Crum, Riffenburgh, Wegner, Agan, Tasker, Spooner, Armstrong, Fraser, & Wallace, 2006; Giordano, Gifford, White, Suarez, Almazor, Rabeneck, Hartman, Backus, Mole, & Morgan, 2007; Mayer, 2011), lowering infectiousness (Andrews, Wood, Bekker, Middlekoop, Walensky, 2012), and curbing secondary transmissions due to suppressed HIV loads (Jenness, Myers, Neaigus, Lulek, Navejas, & Raj-Singh, 2012 according to Quinn, Wawer, Sewankambo, Serwadda, Wabwire-Mangen, Mehan, Lutalo & Gray (2000). The scale up of ART has revolutionized AIDS responses by producing large-scale results, having already saved 9 million life-years is sub-Saharan Africa (UNAIDS, 2012), it is projected to save many more, since 70 percent of individuals maintaining ART treatment are expected to be alive after five years (Verguet, Lim, Murray, Gakidou, & Salomon, 2013). Conversely, delayed ART initiation is associated with hastened mortality and continued risky behavior, thus weakening preventative efforts. With the majority of individuals beginning ART too late, many researchers report an increased chance of lower CD4 counts. Fairall et al. (2008), Keiser et al. (2008), & Kigozi et al. (2009), Bassett et al., 2010 (as cited in Mugglin, Estill, Wandeler, Bender, Egger, Gsponer, & Keiser, 2012). Lawn et al. (2005) and Losina et al. (2010) (as cited in Kayigamba, Bakker, Fikse, Mugisha, & Asiimwe, 2012) report an increased risk of opportunistic infections and ultimately higher mortality rates. Even delaying treatment a few months can be deleterious for individuals with low CD4 cell counts, considering that more than half of the individuals who seek ART for the first time have a CD4 count <100 (Larson, Brennan, McNamara, Long, Rosen, Sanne, & Fox, 2010). A Center for Disease Control and Prevention (CDC) study in the United States found that almost 30% of HIV+ individuals delayed entry to medical care more than three months after being diagnosed (Reed, Hanson, McNaghten, Bertolli, Teshale, Gardner, & Sullivan, 2009).
The goal of the current study was to determine trends associated with linkage to care when using mobile HCT combined with a call-centre approach. Specific questions addressed were: (1) What percentage of HIV positive individuals reached in the mobile HCT program linked to care, 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 in establishing linkage to care similar or different for various demographic groups?
The Call-Centre Approach
Each day, the call centre receives the names and contact information of HIV+ individuals tested during mobile HCT outreach. Staff at the call centre make follow-up telephone calls and provide information on clinic enrolment in a supportive and understanding manner, while emphasizing the importance to link to care. Call centre staff continue to be in contact with patients until they are successfully linked to a clinic. This call centre approach is different from the way other call centres operate in the sense that most of the calls are outbound, though not exclusviely so: Individuals can also call into the call centre or send a free “please call me” text message, whereupon a call centre staff member will contact them.
Research Setting and Procedures
This secondary data analysis reviews data collected during seven months of operation of an HIV Linkage to Care call centre. The sample was 1096 individuals who tested HIV+ during mobile HCT outreach in Limpopo and Gauteng provinces in South Africa between April 1, 2012 and October 31, 2012; data was gathered for an additional five weeks after the end of October in order to track success in linking individuals to care. “Linkage to care” was defined as connecting an HIV+ individual to medical care so that CD4 cell test results are obtained and ART eligibility assessed. Individuals whose CD4 cell count remained high were encouraged to continue counseling and follow-up services, while those meeting eligibility requirements were encouraged to begin ART. De-identified administrative data of the call centre was provided for analysis. The institutional review board at the University of Louisville in Louisville, Kentucky, U.S.A., reviewed this secondary study for human subject protection.
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. CHAID is similar to regression analysis in that it selects the predictors that best account for the variance in a dependent variable, in this case linkage status (e.g. linkage status “linked” or “not linked”). However, CHAID goes a step further by identifying those variables (e.g. age, gender, and language) that most differentiate clients who are linked to care from those who are not linked to care.
Description of sample demographics
The majority of individuals (68.7%) referred to the call centre during the study period were female and 31.3% 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). English was the language of choice with 41.9%, followed by Sepedi (36.5%), Sesotho (4.9%), Tsonga (3.5%), Venda (2%), Setswana (1.6%) and isiZulu (.2%).The language was not known for 7% and the category “other language” accounted for less than 1%. The team from the Gauteng province call centre was responsible for 621 (56.7%) of individuals and the team from the Limpopo province dealt with 475 (43.3%) of individuals.
Percentage of individuals linked to care and trends in rates and demographics differences
Linkage to care was established for 563 (51.4%) of HIV+ individuals referred to the call centre during the seven-month sample. The large majority (95.9%) of those for whom linkages were established were newly diagnosed as HIV+. The HIV status of those not linked to care prior to current testing was unknown. More females (403 or 53.5% out of 753) than males (160 or 46.61% out of 343) (χ2=.037;df=1) were linked to care. 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). The mean CD4 count for males was 331.06 (SD=205.53) and for females 439.72 (SD=231.75) (t(548)=5.107 , p<.001), indicating that males require on average more immediate ART intervention than females.
Using the linkage status (Figure 1) (linked and not linked) as the dependent variable and age, gender and team” (urban and rural) as the independent variables, a CHAID analysis was conducted to identify significant differences in cohorts defined by categories of the independent variables. “Rural” referred to an area in Limpopo Province with intermediate population density, and “urban” to a metropolitan area in Gauteng Province. Gender was the most predictive of the three independent variables and more females were linked to service (53.5%) than males (46.6%). 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 to care than the urban team (32.9%).
Reasons for not being linked to care within 30 days of referral to the call centre were recorded for 442 people (See Figure 2). These individuals could have been linked to care within the seven-month period of the study, but the 30-day cut-off was used to identify a group at risk for not being linked to care. Of the 442 individuals, most (56.3%) were contacted many times but they still did not follow through with a visit to a clinic. The second reason most often recorded (17.9%) for not being linked to care in this group was incorrect contact information. Just over fourteen percent (14.5%) asked not to be called and 11.3% had no telephone, the only means available for the call centre to contact individuals. Reasons for not being able to link to care differed significantly for the two call centres. The urban team made a higher percentage of repeated attempts to contact individuals without success (64.7%) than the rural team (44.9%). The rural team had a higher percentage of individuals referred to them without a telephone (20.3%) as opposed to the urban team (4.7%).
Time to link different cohorts of individuals to care
The mean time to link individuals was 31.1 days (SD=28.6). A CHAID analysis was done by using “days to link” (with two categories) “0-14 Days” and “>14 Days” as the dependent variable. Independent variables were “month linked”, “CD4 count” and “team” (urban and rural) (See Figure 3). It took longer than 14 days for most people to be linked during April, May, and June (77.5% were linked after 14 days), but this changed for later months. During August, September, and October, 50.9% were linked to services within 14 days. The success achieved in later months, however, was not equal across all cohorts. Those with CD4 cell counts of <350 were not linked within the same time frame as those with higher CD4 cell counts during the period August to October. About 40% of individuals with low counts were linked within 14 days in August, September, and October, in contrast to about 60% of those with higher CD4 counts linked in the same timeframe. There were no differences in CD4 cell-count groups during other months. July appears to be a transitional month in the sense that the percentage of individuals linked within 14 days was higher than the percentage during the months of April, May, and June, but lower than the percentage of individuals linked during August, September, and October. July was also the only month with a significant difference in the percentage of individuals linked to care by the urban and rural teams, with the rural team linking 46.5% of individuals and the urban team 28%.
Summary and Discussion:
Results (just over half of HIV+ individuals linked to care) compared favorably with previous studies on mobile HCT, which reported lower linkage to care despite services being free and in close proximity (Krazner, Zeinecker, Ginsbert, Orrell, Kalawe, Lawn, Bekker, & Wood, 2012; MacPherson, Corbett, Makombe, van Oosterhout, Manda, Choko, Thindwa, Swuire, Mann, & Lalloo, 2012). Bassett (2013) reported only 10% linkage to care for community-based mobile testers, although from a larger sample size and with a shorter collection period. Krazner et al. (2010), using a measurement period of six months, found higher rate of linkage among individuals testing through sexually transmitted infection services (84.1%) but found linkage rates for individuals testing on their own initiative were similar to what we found (53.4%). Losina et al. (2010) found that although about 54% engaged in CD4 cell testing, only about 47% of those returned for their CD4 cell results. In our study, 4.1% of individuals reported having previously been diagnosed as HIV positive, while other similar studies found 6.5% (Larson et al., 2010) and 36% (van Rooyen et al., 2013) of their sample had been previously diagnosed.
Most individuals referred to the call centre during the data collection period were female, who were more likely to link to care than males; yet females under 23 had a lower percentage linked to care than all other female age groups (with the rural team linking more young females to care than the urban team). While the average amount of time for a patient to link to care was 31.1 days, linkage to care varied by month, with more individuals linking in less than 14 days during August, September, and October. July was also the only month with a significant difference in the percentage of individuals linked to care by the urban and rural teams, and more individuals linked to care in the warmer climate during this month. The average CD4 count for males was lower than females, indicating that more males in the group that were linked to care required ART interventions sooner than females.
There are several limitations to this study. First, the study does not include a comparison of linkage rates before and after the call centre 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 timeframe. One possible seasonal factor was identified: individuals in a colder climate during the middle of winter linked at a lower rate. A study over 12 months may detect other possible seasonal patterns. Fourth, limited information was obtained for the group not linked to care.
This study also had strengths. The sample size (n=1,096), although not large, is larger than many other HIV linkage to care studies that tracked individuals for a comparable length of time: Losina et al. (2010) with 454 HIV-positive patients in the Durban area; Rooyen et al. (2013) with 201 HIV-positive patients (also in KwaZulu-Natal). The sample we studied had the added advantage of diversity, as participants were from both urban and rural areas and included participants with a wide range in age and languages.
Study findings offer useful insights that can inform future studies 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. Also, as definitions of successful linkage to care presently vary, future studies should use a standardized definition of delayed care (Finnie et al., 2009); without such a benchmark, an important piece of HIV prevention may remain in the shadows.