AIDS Research and Treatment

AIDS Research and Treatment / 2012 / Article
Special Issue

Antiretroviral Treatment in Resource-Limited Settings 2012

View this Special Issue

Clinical Study | Open Access

Volume 2012 |Article ID 375217 | 10 pages | https://doi.org/10.1155/2012/375217

Loss to Followup in HIV-Infected Patients from Asia-Pacific Region: Results from TAHOD

Academic Editor: Anthony Harries
Received26 Oct 2011
Accepted14 Dec 2011
Published22 Feb 2012

Abstract

This study examined characteristics of HIV-infected patients in the TREAT Asia HIV Observational Database who were lost to follow-up (LTFU) from treatment and care. Time from last clinic visit to 31 March 2009 was analysed to determine the interval that best classified LTFU. Patients defined as LTFU were then categorised into permanently LTFU (never returned) and temporary LTFU (re-entered later), and these groups compared. A total of 3626 patients were included (71% male). No clinic visits for 180 days was the best-performing LTFU definition (sensitivity 90.6%, specificity 92.3%). During 7697 person-years of follow-up, 1648 episodes of LFTU were recorded (21.4 per 100-person-years). Patients LFTU were younger ( ), had HIV viral load ≥500 copies/mL or missing ( ), had shorter history of HIV infection ( ), and received no, single- or double-antiretroviral therapy, or a triple-drug regimen containing a protease inhibitor ( ). 48% of patients LTFU never returned. These patients were more likely to have low or missing haemoglobin ( ), missing recent HIV viral load ( ), negative hepatitis C test ( ), and previous temporary LTFU episodes ( ). Our analyses suggest that patients not seen at a clinic for 180 days are at high risk of permanent LTFU, and should be aggressively traced.

1. Introduction

Loss to followup (LTFU) in patients receiving antiretroviral therapy can cause serious consequences such as discontinuation of treatment and increased risk of death [13]. At a program level, LTFU can make it difficult to evaluate outcomes of treatment and care [4, 5]. In resource-limited settings, where treatment has become rapidly available following the rollout of antiretroviral therapy, LTFU presents even more challenging obstacles that require special consideration and approaches [6, 7].

One of the key questions in patient followup is how to define a patient as LTFU. This has varied in studies conducted in different settings [810]. Defining LTFU using a very early threshold, for example, a patient with no clinic visit in the last three months, may result in many patients being considered as LTFU who would return to clinic naturally at a later date. Defining LTFU with a long threshold, for example, one year, may mean delaying too long before any effort is made to track patients potentially at risk of LTFU.

The majority of research into LTFU in HIV-infected patients receiving antiretroviral treatment in resource-limited settings has been conducted in the sub-Saharan Africa region [3, 1013]. A few studies have been conducted among Asian, mostly female, patients [1416]. Using data from the TREAT Asia HIV Observational Database (TAHOD), this study was carried out to find the best-performing definition of LTFU and examine the characteristics of HIV-infected patients from the Asia-Pacific who were LTFU from treatment and care.

2. Methods

Established in 2003, TAHOD is a collaborative observational cohort study involving 18 sites in the Asia-Pacific region (see Acknowledgement). Detailed methods have been published previously [17]. Briefly, each site recruited approximately 200–300 HIV-infected patients, with recruitment based on a consecutive series of patients regularly attending a given site from a particular start-up time. Ethical approval for the study was obtained from the University of New South Wales Ethics Committee, Western Institutional Review Board, and respective local ethics committee from each TAHOD participating site.

The following data were collected: patient demographics and baseline data, CD4 and CD8 count, HIV viral load, prior and new AIDS defining illness (ADI), date and cause of death, prior and current prescribed antiretroviral treatment (ART), and reason for treatment change. Data were collected according to a common protocol. Upon recruitment, all available data prior to entry to TAHOD (considered as retrospective data) were extracted from patient case notes. Prospective data were updated six-monthly at each clinic and transferred to the data management centre for aggregation and analyses in March and September each year. TAHOD sites were encouraged to contact patients who have not been seen in the clinics in the previous 12 months.

TAHOD data submitted at March 2009 and March 2010 were used to find the best-performing definition of LTFU. TAHOD patients who had no followup after recruitment were not included in this analysis. Patients who were not seen in clinic for more than 12 months prior to the March 2010 data submission (i.e., last clinic visit prior to March 2009) were considered to be truly LTFU. The days between the last clinical visit and 31 March 2009 in the March 2009 data transfer were then used to find the interval that best classified a true LTFU in the following way. A series of cutoffs were considered, from ten to 365 days, to define patients as potentially LTFU. Each of these definitions of potential LTFU was compared with the gold standard of true LTFU, defined as no patient followup in the 12 months prior to 31 March 2010. The sensitivity and specificity of each cutoff in identifying true LTFU were calculated, and the best performing cutoff identified using the area under the receiver operator characteristic (ROC) curve. The optimal definition of LTFU identified in terms of maximising the sensitivity and specificity of true LTFU was found to be 180 days (see Results). This definition was then used in the risk factor analyses that follow.

Followup started from the last clinic visit at the March 2007 data submission. Patients who were considered LTFU before March 2007 (i.e., had no clinic visits 180 days before 31 March 2007) were excluded from the analysis. For patients enrolled after March 2007, the followup started at the time of enrolment. In terms of calculating person-years of followup, the end of followup for patients who had no clinic visit for 180 days and so were considered as LTFU was defined as 90 days after their last clinic visit. For patients not considered LTFU, the end of followup was also defined as 90 days after their last clinic visit. If a patient died, the followup was censored on the date of death if the date was within 180 days of their last clinic visit. Patients who died after March 2007 were considered to have complete followup. It should be noted that patients who were considered LTFU could return to clinic and reenter followup. The start of this reentry to followup was defined as 3 months prior to the first clinic visit that reinitiated followup. The patients that reentered followup could also be re-LTFU if the patient subsequently did not attend clinic for more than 180 days. The definitions we adopted were consistent with those in a previous study [18].

The rates of LTFU were calculated by the number of total LTFU periods divided by the total duration of followup contributed by the patients included in the analysis [18]. Because of the reentering and re-LTFU, patients could contribute more than one episode of LTFU in this analysis. The rates were further calculated in different strata, including age, sex, exposure category, hepatitis B and C infection, year since HIV infection, calendar year, the latest CD4 count and viral load, antiretroviral treatment status, CDC disease stages, prophylaxis (coded as receiving or not), and haemoglobin level, all taken at the start of each episode.

Factors associated with LTFU were assessed by multivariate Poisson regression models, using generalised estimating equations, to allow for multiple events of LTFU in the same patients. CD4 count, HIV viral load, antiretroviral treatment, AIDS diagnosis, and haemoglobin tests were included as time-dependent variables and updated at the time the new measurement or diagnosis was available.

Patients who had at least one episode of LTFU were then categorised into two groups: those who had no more clinical visits in the database (permanently LTFU) and those who later reentered followup (temporary LTFU). Multivariate logistic regression models were used to compare the characteristics in patients who were considered permanently LTFU with those who were temporary LTFU. All covariates were taken at the end of the episode in patients with truly LTFU or at the end of the first episode in patients considered temporary LTFU.

Multivariate models were built using a forward-stepwise approach. The final model included covariates that remained significant at the level. Nonsignificant variables were also presented and adjusted for the final multivariate models. Data management and statistical analyses were performed using SAS for Windows (SAS Institute Inc., Cary, NC, USA) and Stata (StataCorp, STATA 10.1 for Windows, College Station, TX, USA).

3. Results

In March 2007, there were 2565 patients in the database. 1061 patients were subsequently enrolled in TAHOD up to March 2010. A total of 3626 patients from TAHOD who had follow-up visits in the clinic were included in this analysis. During the study period (from March 2007 to March 2010), there were 54 patients who died and considered to have complete followup.

Using days between last clinic visit and 31 March 2009 in the March 2009 data transfer, we identified the interval that best classifies a true LTFU (i.e., no clinic visit after 31 March 2009). An interval of 180 days was determined as the best-performing definition (Table 1, sensitivity 90.6%, specificity 92.3%). Using 180 days as the LTFU cutoff, during 7697 person-years of followup, a total of 1648 episodes of LTFU from 1298 patients were identified, giving a crude LTFU rate of 21.4 per 100 person-years (95% confidence interval, CI, 20.4 to 22.5). Of those 1648 episodes of LTFU identified using 180 days as the cutoff, 48% were considered permanently LTFU (i.e., the patient did not return to clinic before 31 March 2010), corresponding to 45% of the 1298 patients.


Cutoff (days)Sensitivity (%)Specificity (%)Area under ROCCutoff (days)Sensitivity (%)Specificity (%)Area under ROC

1099.6716.9758.3216090.9690.7790.87
2099.0224.3261.6717090.6491.4491.04
3098.0531.3164.6817590.6492.0591.34
4096.8239.9068.36180 90.55 92.26 91.41
5096.3449.5272.9318590.2392.5391.38
6095.7757.2076.4819089.3393.0191.17
7095.2865.5280.4020088.5293.4490.98
8095.1171.2683.1921087.7994.1390.96
9094.7177.6286.1624085.2695.2590.26
10094.2280.9187.5727083.5596.4389.99
12093.2486.1889.7130082.0097.0489.52
15091.5390.1790.8536578.9997.7388.36

True LTFU defined as no patient followup in the 12 month prior to 31 March 2010. Each cutoff used as a potential definition of LTFU was the days between last clinical visit and 31 March 2009 in the March 2009 data transfer. The sensitivity and specificity of each cutoff in identifying true LTFU were calculated, and the optimal cutoff identified based on ROC analysis.

The patient characteristics are summarised in Table 2. The majority of patients were male (71%), aged between 36 and 45 years (40%), and reported heterosexual transmission (64%). Chinese (27%), Thai (26%), and Indian (11%) were the main ethnic groups. At recruitment, approximately 12% did not have a CD4 count test, and of those tested, the majority had a CD4 count more than 200 cells/μL. Nearly half (45%) did not have an HIV viral load test, and of those tested, the majority were below 500 copies/mL. Close to half of the patients (46%) were diagnosed with an AIDS defining illness at recruitment, with tuberculosis being the main illness. Most patients (63%) had been reported to be diagnosed with HIV for less than 6 years when recruited to TAHOD (measured as the time from first reported positive HIV test). Less than 10% of the patients were coinfected with either hepatitis B or hepatitis C. At recruitment, the majority of patients had normal haemoglobin level. At the start of study followup, most of the patients were on antiretroviral therapy including three or more drugs in combination including at least one nucleoside reverse transcriptase inhibitor (NRTI) and one nonnucleoside reverse transcriptase inhibitor. Over 20% of patients were in a combination with at least one NRTI and a protease inhibitor (PI). All patients were receiving, or started, antiretroviral therapy during followup.


Total3626
CharacteristicsNumber%

Sex
 Male256771
 Female105929

Current age (years)
≤35138338
36–45144940
46+79422

Reported exposure
 Heterosexual contact233764
 Homosexual contact74921
 Injecting drug use2637
 Other/unknown2778

Ethnicity
 Chinese98927
 Indian39011
 Thai93326
 Other/unknown131436

Baseline CD4 count (cells/μl.)
 ≤1002397
 101–20040611
 201+253170
 Missing45012

Baseline HIV RNA (copies/ml)
 ≤500148241
 501+37910
 Missing176549

CDC disease stage at baseline
 Stage A162145
 Stage B3219
 Stage C168446

Tuberculosis diagnosis at baseline
 No275876
 Yes86824

Time since HIV infection (years)
 ≤5229563
 6+124634
 Missing852

Hepatitis B infection
 No229763
 Yes2577
 Not tested107230

Hepatitis C infection
 No200755
 Yes3249
 Not tested129536

Anemia at baseline
 No248068
 Yes59716
Haemoglobin not tested56716
Antiretroviral treatment at baseline
 3 + (NRTI + NNRTI)222461
 3 + (NRTI + PI)74421
 No/mono/double drug58316
 3 + (other combination)752

Anemia: haemoglobin <13 g/dl (male), <11 g/dl (female); NRTI: nucleoside reverse transcriptase inhibitor; NNRTI: nonnucleoside reverse transcriptase inhibitor; PI: protease inhibitor.

Table 3 summarises univariate and multivariate analyses of factors associated with LTFU using 180 days as cut-off. In univariate analyses, the rate of LTFU was significantly lower in patients with a current CD4 counts above 200 cells/μL compared to patients with a CD4 count less than 100 cells/μL, but this was not significant in the final multivariate model. In the final multivariate model (Table 3), factors associated with LTFU included age (younger patients had higher rate of LTFU), current HIV viral load (either patients with HIV viral load ≥500 copies/mL or no tests in recent 180 days had higher rate of LTFU), history of HIV infection (patients with shorter history of HIV infection had higher rate of LTFU), hepatitis C infection (patients with positive hepatitis C antibody had higher rate of LTFU), and, finally, current combination of antiretroviral treatment (compared to patients on triple-drug regimen with at least one NRTI and one NNRTI, patients receiving no-, single-, or double-drug antiretroviral therapy, or a triple-drug regimen containing at least one NRTI and one PI, had higher rate of LTFU).


Person- yearsNumber LTFUCrude Rate1Adjusted
95% CIIRR295% CI valueIRR295% CI value

Sex
 Male5468.1120622.06(20.85, 23.34)1.001.00
 Female2229.244219.83(18.06, 21.77)1.10(0.98, 1.24)0.0901.04(0.93, 1.17)0.446

Current age (years)
 ≤352210.457526.01(23.97, 28.23)1.001.000.0023
 36~453320.271821.62(20.10, 23.27)0.82(0.74, 0.92)0.0010.89(0.79, 1.00)0.050
 46+2166.635516.39(14.77, 18.18)0.69(0.60, 0.79)<0.0010.76(0.66, 0.88)<0.001

Reported exposure
 Heterosexual  contact5144.598519.15(17.99, 20.38)1.001.00
 Homosexual  contact1707.234420.15(18.13, 22.40)1.10(0.93, 1.29)0.2751.05(0.89, 1.25)0.540
 Injecting drug use344.312536.31(30.47, 43.27)1.21(0.97, 1.51)0.0981.10(0.86, 1.40)0.437
 Other/unknown501.319438.70(33.62,44.55)1.64(1.37, 1.98)<0.0011.56(1.29, 1.88)<0.001

Current CD4 count (cells/μl.)
 ≤100233.76929.52(23.32, 37.38)1.001.00
 101–200635.713621.40(18.09, 25.31)0.92(0.68, 1.22)0.5510.96(0.72, 1.29)0.800
 201+6327.6118118.66(17.63, 19.76)0.75(0.58, 0.96)0.0230.79(0.61, 1.02)0.071
 Missing500.326252.37(46.40, 59.11)1.18(0.90, 1.55)0.2350.99(0.74, 1.31)0.922

Current HIV RNA (copies/ml)
 ≤5004213.767916.11(14.95, 17.37)1.001.000.0213
 501+537.115829.42(25.17, 34.38)1.71 (1.43, 2.04)<0.0011.24(1.03, 1.51)0.026
 Missing2946.481127.52(25.69, 29.49)1.75(1.55, 1.98)<0.0011.64(1.45, 1.86)<0.001

CDC disease stage
 Stage A3205.182825.83(24.13, 27.65)1.001.00
 Stage B801.611814.72(12.29, 17.63)0.93(0.76, 1.14)0.5070.95(0.77, 1.17)0.623
 Stage C3690.570219.02(17.67, 20.48)0.84(0.75, 0.93)0.0010.92(0.82, 1.02)0.125

Tuberculosis diagnosis
 Yes1806.737220.59(18.60, 22.79)1.001.00
 No5890.6127621.66(20.51, 22.88)1.04(0.92, 1.18)0.5370.98(0.87, 1.12)0.801

Time since HIV infection (years)
 ≤53477.278522.58(21.05, 24.21)1.001.000.0053
 6+4115.784420.51(19.17, 21.94)0.84(0.75, 0.94)0.0020.89(0.79, 1.00)0.048
 Missing104.31918.21(11.61, 28.55)0.58(0.36, 0.94)0.0270.49(0.30, 0.79)0.004

Hepatitis B infection
 Yes584.511219.16(15.92, 23.06)1.001.00
 No5101.988317.31(16.20, 18.49)0.93(0.76, 1.13)0.4740.90(0.74, 1.10)0.319
 N/A2010.865332.48(30.08, 35.06)0.98(0.80, 1.21)0.8591.07(0.85, 1.35)0.548

Hepatitis C infection
 Yes541.414927.52(23.44, 32.31)1.001.000.0303
 No4692.879616.96(15.82, 18.18)0.81(0.67, 0.98)0.0290.81(0.67, 0.98)0.034
 N/A2463.070328.54(26.51, 30.73)0.75(0.62, 0.91)0.0040.77(0.63, 0.93)0.008
Current anemia (male < 13 g/dl, female < 11 g/dl)
 Yes1021.115515.18(12.97, 17.77)1.001.00
 No5771.6115720.05(18.92, 21.24)1.09(0.92, 1.30)0.3021.11(0.94, 1.32)0.227
 N/A904.533637.15(33.38, 41.34)1.31(1.07, 1.59)0.0081.09(0.89, 1.34)0.382
Current ART4
 3 + (NRTNRTI)4830.894219.50(18.29, 20.79)1.001.000.0013
 3 + (NRTI + PI)1898.337719.86(17.95, 21.97)1.21(1.06, 1.38)0.0051.22(1.07, 1.39)0.003
 No/mono/double ARV762.730039.33(35.12, 44.05)2.18(1.90,2.50)<0.0011.92(1.66, 2.22)<0.001
 3 + (other combination)205.42914.12(9.81, 20.32)0.95(0.65, 1.38)0.7861.01(0.69, 1.47)0.975

(1) Crude rate, per 100 person-years.
(2) Stratified by TAHOD sites.
(3) Overall for test for trend (ordinal categorical covariates) or for homogeneity (nominal categorical covariates).
(4) ART: NRTI: nucleoside reverse transcriptase inhibitor; NNRTI: nonnucleoside reverse transcriptase inhibitor; PI: protease inhibitor.

Table 4 shows factors that predict permanent LTFU among patients who had no clinic visit for 180 days and so met our optimal definition of LTFU. In the final multivariate model, patients permanently LTFU were more likely to be older, have not been anemic, have no recent HIV viral load test, have tested negative for hepatitis C infection or have never tested for hepatitis C, and have had more than one episode of previous temporary LTFU.


NumberTrue loss%OR195% CI valueAdjusted OR195% CI value

Sex
 Male120658448.41.001.00
 Female44220947.30.89(0.69, 1.15)0.3590.80(0.61, 1.05)0.104

Current age (years)
 ≤3556827848.91.001.000.0972
 36~4571734047.41.33(1.03, 1.71)0.0311.31(1.00, 1.72)0.050
 46+36317548.21.27(0.94, 1.72)0.1181.28(0.93, 1.77)0.128

Reported exposure
 Heterosexual contact98544345.01.001.00
 Homosexual contact34419957.81.12(0.78, 1.60)0.5321.24(0.85,1.81)0.262
 Injecting drug use1255544.01.01(0.59, 1.73)0.9691.32(0.72, 2.41)0.364
 Other/unknown1949649.51.07(0.69, 1.64)0.7731.22(0.78, 1.93)0.382

Current CD4 count (cells/μl.)
 ≤100583662.11.001.00
 101–2001296651.20.76(0.36, 1.60)0.4710.99(0.47, 2.13)0.989
 201+106846543.50.62(0.33, 1.18)0.1440.82(0.42, 1.59)0.551
 Missing39322657.51.50(0.77, 2.93)0.2381.18(0.58, 2.42)0.649

Current HIV RNA (copies/mL)
 ≤50059823038.51.001.000.0112
 501+1537851.01.02(0.68, 1.52)0.9240.94(0.62, 1.42)0.767
 Missing89748554.12.13(1.63, 2.80)<0.0011.54(1.13, 2.09)0.006

CDC disease stage
 Stage A82841349.91.001.00
 Stage B1215444.60.77(0.48, 1.22)0.2580.70(0.43, 1.14)0.154
 Stage C69932646.61.00(0.78, 1.27)0.9751.05(0.81, 1.36)0.702

Tuberculosis diagnosis
 Yes36118651.51.001.00
 No128760747.20.87(0.66, 1.16)0.3420.85(0.63, 1.15)0.297

Time since HIV infection (years)
 ≤577140051.91.001.00
 6+85838945.31.25(0.98, 1.60)0.0761.03(0.79, 1.34)0.835
 Missing19421.10.37(0.12, 1.17)0.0910.43(0.13, 1.43)0.170

Hepatitis B infection
 Yes1124742.01.001.00
 No88343148.81.30(0.84, 2.03)0.2431.35(0.84, 2.16)0.222
 N/A65331548.21.31(0.82, 2.09)0.2531.03(0.60,1.76)0.908
Hepatitis C infection
 Yes1496644.31.001.000.0042
 No79637647.21.57(1.01, 2.45)0.0461.66(1.04, 2.66)0.034
 N/A70335149.91.96(1.26, 3.05)0.0032.16(1.35, 3.46)0.001

Current anemia (male < 13 g/dL, female < 11 g/dL)
 Yes1418761.71.001.00<0.0012
 No106545642.80.53(0.35, 0.81)0.0030.50(0.32, 0.76)0.001
 N/A44225056.61.15(0.73, 1.81)0.5490.78(0.49, 1.26)0.310

Current ART**
 3 + (NRTI + NNRTI)91140444.31.001.00
 3 + (NRTI + PI)35616746.90.76(0.57, 1.02)0.0720.74(0.54,1.01)0.057
 No/mono/double ARV35220959.40.93(0.69, 1.26)0.6440.78(0.57, 1.08)0.137
 3 + (other combination)291344.80.89(0.40, 1.98)0.7700.85(0.38, 1.94)0.707
Previous episode of temporary LTFU
 None129858945.41.001.00<0.0012
 Once29615853.42.79(2.05, 3.80)<0.0012.71(1.97, 3.72)<0.001
 Twice544685.231.76(13.91, 72.52)<0.00127.75(12.03, 64.01)<0.001

(1) Stratified by TAHOD sites.
(2) Overall for test for trend (ordinal categorical covariates) or for homogeneity (nominal categorical covariates).
(3) ART: NRTI: nucleoside reverse transcriptase inhibitor; NNRTI: nonnucleoside reverse transcriptase inhibitor; PI: protease inhibitor.

4. Discussion

We found that an interval of 180 days between clinic visits was the best-performing definition of LTFU based on sensitivity and specificity in identifying true LTFU. By this definition, we observed that approximately one in five patients in our cohort would miss clinic visits for more than 180 days and so become defined as LTFU. Among these patients in our cohort close to half eventually returned to followup, with half becoming truly lost to HIV-related treatment and care.

The 180-day cutoff has been used by other studies as a working definition of LTFU [10, 1921]. Other intervals have also been proposed as measurements of classifications of LTFU, such as 90 days [8] and 365 days [9]. Regional- and cohort-dependent characteristics, such as scheduled clinic visits, patient burden, and drug availability could result in specific intervals that best categorise patients at risk of LTFU. Nevertheless, a 180-day (or 6-month) cutoff is an appealing and easy-to-apply definition that could be used in different clinical settings in the Asia-Pacific region to flag patients at risk of being permanently lost to treatment and care. Our analyses suggest patients with no clinic visits for six months are at high risk of being permanently lost and should be aggressively traced.

Chi et al. also found that a cutoff of 180 days was optimal to define LTFU after analysing data from the Africa, Asia, and Latin America regions of the IeDEA collaboration (including data from our cohort) [22]. There are some methodological differences between our analyses, principally regarding minimum numbers of patients for site inclusion. Chi et al. found quite extensive heterogeneity between sites, something we also found to a lesser extent. However, it is nevertheless reassuring that we found a similar optimal cutoff of 180 days without clinic visits to define LTFU. With rapid scaling up of antiretroviral treatment taking place globally, there is a need to adopt a universal consistent definition of LTFU, or a general algorithm to define cutoffs, to evaluate HIV treatment programs in different regions [6, 7, 19].

Over one in five patients in our cohort failed to come to clinic for more than 180 days in a given year. Similar rates have also been found in patients from Africa [3, 11]. However, the LTFU rate was lower in EuroSIDA [23], a large prospective cohort study with HIV-infected patients mainly from Europe (using one year as a cutoff). Approximately half of the patients who experienced LTFU in our study later came back to clinic, and patients who had a previous episode of LTFU were more likely to prove to be true LTFU, similar to previous findings [18].

We found that younger patients, patients infected with hepatitis C, and patients with detectable or unmeasured viral load were more likely to experience LTFU. These findings are all consistent with previous study findings [10, 11, 2426]. Patients with undetectable viral load are likely to be motivated and adherent to antiretroviral treatment and thus remain in care. Among those patients who experienced LTFU, we found that those who tested negative for hepatitis C infection or were never tested for hepatitis C were more likely to be permanently LTFU. This finding seems counterintuitive, but it might be that patients who have tested positive for hepatitis C receive more medical attention from their clinicians and thus prove less likely to be permanently LTFU. Among patients identified as LTFU, anemic patients were also more likely to be permanently lost to treatment and care. Anemia has been shown to be a strong prognostic marker for HIV disease progression and survival [27], which could, at least in part, explain these patients failing to return to followup.

Compared to patients on NNRTI-based regimen, patients receiving no-, single-, or double-drug antiretroviral therapy or a triple-drug regimen containing PI were more likely to experience LTFU. The reasons for this are not clear. The greater loss to followup may be associated with increased drug toxicity, either resulting in a patient receiving mono- or dual therapy or from receiving a PI. Patients receiving PI-based regimens are also those who are more likely to be on a second line regimen, a regimen that may be substantially more expensive than first line. In the Asia Pacific region, out-of-pocket expenses are needed to pay for treatment in some clinics. Hence, the lost to followup may be associated with drug availability or affordability. It is worth noting that patients receiving mono- or dual therapy, or a PI based regimen, were also associated with being less likely to be permanently lost to followup, that is to say more likely to return to clinic (albeit not quite statistically significantly so). This possibly supports the idea of these regimens being associated with short-term drug availability or affordability issues. Unfortunately, data are not available to address this issue in any greater detail.

It has been shown that, in resource-limited settings, predominantly in Africa, patients who are LTFU have a much poorer prognosis than patients who remain in followup [5]. In part, this is due to a proportion of patients who die not having vital status information updated at their treatment site. The extent to which this occurs in TAHOD is uncertain. While it seems likely that at least some patients who are LTFU have died without this information reaching the site, the lack of association between key measures of HIV disease progression, such as CD4 count and AIDS defining illnesses, and LFTU suggests it may be lower than in African settings. However, this association between LTFU and poorer prognosis underpins the need for consistent definitions of LTFU in research cohort studies, and where there are possible active patient tracing strategies or at least sampling-based approaches [28] to ensure comparability of results across studies and settings.

Several limitations should be considered in interpreting the results in this paper. First, TAHOD participating sites are generally urban referral centres, and the patients recruited in TAHOD were those regularly attending a given TAHOD site. Hence, TAHOD patients are not representative of all HIV-infected patients in the Asia and Pacific region. The overall rate of LTFU we saw in our study is therefore likely to be an underestimate of rates across the region. However, the effect of these sampling biases on the optimal definition of LTFU and on the covariate analyses is arguably less strong. It is reassuring that our estimate of the optimal definition of LTFU is consistent with that seen across Africa and Latin America [22]. Second, since antiretroviral treatment has become more decentralised and available in distant or rural communities with rapid scale-up programs, patients might choose to receive treatment and care locally rather than at tertiary and referral centres [29, 30]. Consequently, patients may have been retained in care but not necessarily in the clinics involved in this study. Information on referral to other health facility was only recently included in the data collection, so we could not further verify if patients were retained in care or truly loss to health services. Third, we do not collect data on the measures TAHOD sites undertake to routinely trace patients who are LTFU. These measures differ across sites according to local practices and conditions. Effective patient tracking and recording are essential to program evaluation and maintenance of treatment and care [1, 18]. What patient tracking measures are effective in retaining patients in treatment and care in the Asia-Pacific region is an area that deserves further research. We also do not have data on transportation [31], social and economic status [32], pregnancy for women [10], and community support [33], all of which have been found to be important determinants of LTFU. Lastly, the patients included in this study were all receiving, or started, antiretroviral treatment and had clinical assessments. Consequently, the results cannot be extrapolated to patients not yet initiated on antiretroviral therapy. Research into followup among HIV-infected patients not receiving antiretroviral treatment in the Asia-Pacific region needs to be considered [3436], particularly in the context of the move to start treatment earlier.

5. Conclusion

With rapid scaleup of antiretroviral treatment, it is essential to study factors that predict loss to followup and identify patients at risk of loss to treatment and care, particularly in resource-limited settings. At the treatment and care level, this can maintain efficacy of antiretroviral therapy and avoid adverse events. At the program evaluation level, the impact of loss to followup on overall treatment outcome, disease progression, and survival can then be accounted for with appropriate statistical adjustments. Collaboration with HIV treatment programs in other regions in studies on LTFU and in particular standardisation of LTFU definitions are essential for reporting and program evaluation.

Acknowledgments

The TREAT Asia HIV Observational Database and the Australian HIV Observational Database are part of the Asia Pacific HIV Observational Database and are initiatives of TREAT Asia, a program of amfAR, The Foundation for AIDS Research, with support from the following institutes of the US National Institutes of Health (NIH): National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Child Health and Human Development (NICHD), the Office of the Director (OD), and the National Cancer Institute (NCI), as part of the International Epidemiologic Databases to Evaluate AIDS (IeDEA) (Grant no. U01AI069907). Additional support is provided by the Dutch Ministry of Foreign Affairs through a partnership with Stichting Aids Fonds and from the Austrian AIDS Life Association (AALA). The National Centre in HIV Epidemiology and Clinical Research is funded by the Australian Government Department of Health and Ageing and is affiliated with the Faculty of Medicine, The University of New South Wales. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.

References

  1. R. P. Dalal, C. MacPhail, M. Mqhayi et al., “Characteristics and outcomes of adult patients lost to follow-up at an antiretroviral treatment clinic in Johannesburg, South Africa,” Journal of Acquired Immune Deficiency Syndromes, vol. 47, no. 1, pp. 101–107, 2008. View at: Publisher Site | Google Scholar
  2. A. T. Brennan, M. Maskew, I. Sanne, and M. P. Fox, “The importance of clinic attendance in the first six months on antiretroviral treatment: a retrospective analysis at a large public sector HIV clinic in South Africa,” Journal of the International AIDS Society, vol. 13, no. 1, article 49, 2010. View at: Publisher Site | Google Scholar
  3. H. Bygrave, K. Kranzer, K. Hilderbrand et al., “Trends in loss to follow-up among migrant workers on antiretroviral therapy in a community cohort in Lesotho,” PLoS ONE, vol. 5, no. 10, Article ID e13198, 2010. View at: Publisher Site | Google Scholar
  4. M. W. G. Brinkhof, B. D. Spycher, C. Yiannoutsos et al., “Adjusting mortality for loss to follow-up: analysis of five art programmes in sub-saharan africa,” PLoS ONE, vol. 5, no. 11, Article ID e14149, 2010. View at: Publisher Site | Google Scholar
  5. M. W. G. Brinkhof, M. Pujades-Rodriguez, and M. Egger, “Mortality of patients lost to follow-up in antiretroviral treatment programmes in resource-limited settings: systematic review and meta-analysis,” PLoS ONE, vol. 4, no. 6, Article ID e5790, 2009. View at: Publisher Site | Google Scholar
  6. B. H. Chi, R. A. Cantrell, A. Mwango et al., “An empirical approach to defining loss to follow-up among patients enrolled in antiretroviral treatment programs,” American Journal of Epidemiology, vol. 171, no. 8, pp. 924–931, 2010. View at: Publisher Site | Google Scholar
  7. M. Egger, B. D. Spycher, J. Sidle et al., “Correcting mortality for loss to follow-up: a nomogram applied to antiretroviral treatment programmes in sub-Saharan Africa,” PLoS Medicine, vol. 8, no. 1, article e1000390, 2011. View at: Publisher Site | Google Scholar
  8. K. Wools-Kaloustian, S. Kimaiyo, L. Diero et al., “Viability and effectiveness of large-scale HIV treatment initiatives in sub-Saharan Africa: experience from western Kenya,” AIDS, vol. 20, no. 1, pp. 41–48, 2006. View at: Google Scholar
  9. P. Braitstein, M. W. Brinkhof, F. Dabis et al., “Mortality of HIV-1-infected patients in the first year of antiretroviral therapy: comparison between low-income and high-income countries,” The Lancet, vol. 367, no. 9513, pp. 817–824, 2006. View at: Publisher Site | Google Scholar
  10. B. Wang, E. Losina, R. Stark et al., “Loss to follow-up in a community clinic in South Africa-roles of gender, pregnancy and CD4 count,” South African Medical Journal, vol. 101, no. 4, pp. 253–257, 2011. View at: Google Scholar
  11. V. Ochieng-Ooko, D. Ochieng, J. E. Sidle et al., “Influence of gender on loss to follow-up in a large HIV treatment programme in western kenya,” Bulletin of the World Health Organization, vol. 88, no. 9, pp. 681–688, 2010. View at: Publisher Site | Google Scholar
  12. R. Weigel, M. Hochgesang, M. W.G. Brinkhof et al., “Outcomes and associated risk factors of patients traced after being lost to follow-up from antiretroviral treatment in Lilongwe, Malawi,” BMC Infectious Diseases, vol. 11, article 31, 2011. View at: Publisher Site | Google Scholar
  13. O. Keiser, B. H. Chi, T. Gsponer et al., “Outcomes of antiretroviral treatment in programmes with and without routine viral load monitoring in southern Africa,” AIDS, vol. 25, no. 14, pp. 1761–1769, 2011. View at: Publisher Site | Google Scholar
  14. S. Thai, O. Koole, P. Un et al., “Five-year experience with scaling-up access to antiretroviral treatment in an HIV care programme in Cambodia,” Tropical Medicine and International Health, vol. 14, no. 9, pp. 1048–1058, 2009. View at: Publisher Site | Google Scholar
  15. P. L. Toro, M. Katyal, R. J. Carter et al., “Initiation of antiretroviral therapy among pregnant women in resource-limited countries: CD4+ cell count response and program retention,” AIDS, vol. 24, no. 4, pp. 515–524, 2010. View at: Publisher Site | Google Scholar
  16. M. Panditrao, S. Darak, V. Kulkarni, S. Kulkarni, and R. Parchure, “Socio-demographic factors associated with loss to follow-up of HIV-infected women attending a private sector PMTCT program in Maharashtra, India,” AIDS Care, vol. 23, no. 5, pp. 593–600, 2011. View at: Publisher Site | Google Scholar
  17. J. Zhou, N. Kumarasamy, F. Zhang et al., “Predicting short-term disease progression among HIV-infected patients in Asia and the Pacific region: preliminary results from the TREAT Asia HIV Observational Database (TAHOD),” HIV Medicine, vol. 6, no. 3, pp. 216–223, 2005. View at: Publisher Site | Google Scholar
  18. T. Hill, L. Bansi, C. Sabin et al., “Data linkage reduces loss to follow-up in an observational HIV cohort study,” Journal of Clinical Epidemiology, vol. 63, no. 10, pp. 1101–1109, 2010. View at: Google Scholar
  19. M. W. G. Brinkhof, F. Dabis, L. Myer et al., “Early loss of HIV-infected patients on potent antiretroviral therapy programmes in lower-income countries,” Bulletin of the World Health Organization, vol. 86, no. 7, pp. 559–567, 2008. View at: Publisher Site | Google Scholar
  20. E. H. Geng, N. Emenyonu, M. B. Bwana, D. V. Glidden, and J. N. Martin, “Sampling-based approach to determining outcomes of patients lost to follow-up in antiretroviral therapy scale-up programs in Africa,” Journal of the American Medical Association, vol. 300, no. 5, pp. 506–507, 2008. View at: Publisher Site | Google Scholar
  21. C. Cesar, B. E. Shepherd, A. J. Krolewiecki et al., “Rates and reasons for early change of first HAART in HIV-1-infected patients in 7 sites throughout the Caribbean and Latin America,” PLoS ONE, vol. 5, no. 6, Article ID e10490, 2010. View at: Publisher Site | Google Scholar
  22. B. H. Chi, C. T. Yiannoutsos, A. O. Westfall et al., “Universal definition of loss to follow-up in HIV treatment programs: a statistical analysis of 111 facilities in Africa, Asia, and Latin America,” PLoS Medicine, vol. 8, no. 10, article e1001111, 2011. View at: Publisher Site | Google Scholar
  23. A. Mocroft, O. Kirk, P. Aldins et al., “Loss to follow-up in an international, multicentre observational study,” HIV Medicine, vol. 9, no. 5, pp. 261–269, 2008. View at: Publisher Site | Google Scholar
  24. R. Zachariah, K. Tayler-Smith, M. Manzi et al., “Retention and attrition during the preparation phase and after start of antiretroviral treatment in Thyolo, Malawi, and Kibera, Kenya: implications for programmes?” Transactions of the Royal Society of Tropical Medicine and Hygiene, vol. 105, no. 8, pp. 421–430, 2011. View at: Publisher Site | Google Scholar
  25. T. Hill, L. Bansi, C. Sabin et al., “Data linkage reduces loss to follow-up in an observational HIV cohort study,” Journal of Clinical Epidemiology, vol. 63, no. 10, pp. 1101–1109, 2010. View at: Google Scholar
  26. A. Mocroft, O. Kirk, P. Aldins et al., “Loss to follow-up in an international, multicentre observational study,” HIV Medicine, vol. 9, no. 5, pp. 261–269, 2008. View at: Publisher Site | Google Scholar
  27. J. D. Lundgren and A. Mocroft, “Anemia and survival in human immunodeficiency virus,” Clinical Infectious Diseases, vol. 37, no. 4, pp. s297–s303, 2003. View at: Publisher Site | Google Scholar
  28. C. T. Yiannoutsos, M. W. An, C. E. Frangakis et al., “Sampling-based approaches to improve estimation of mortality among patient dropouts: experience from a large PEPFAR-funded program in Western Kenya,” PLoS ONE, vol. 3, no. 12, Article ID e3843, 2008. View at: Publisher Site | Google Scholar
  29. M. Bedelu, N. Ford, K. Hilderbrand, and H. Reuter, “Implementing antiretroviral therapy in rural communities: the Lusikisiki model of decentralized HIV/AIDS care,” Journal of Infectious Diseases, vol. 196, no. 3, pp. S464–S468, 2007. View at: Publisher Site | Google Scholar
  30. A. K. Chan, G. Mateyu, A. Jahn et al., “Outcome assessment of decentralization of antiretroviral therapy provision in a rural district of Malawi using an integrated primary care model,” Tropical Medicine and International Health, vol. 15, supplement 1, pp. 90–97, 2010. View at: Publisher Site | Google Scholar
  31. E. H. Geng, D. R. Bangsberg, N. Musinguzi et al., “Understanding reasons for and outcomes of patients lost to follow-up in antiretroviral therapy programs in Africa through a sampling-based approach,” Journal of Acquired Immune Deficiency Syndromes, vol. 53, no. 3, pp. 405–411, 2010. View at: Publisher Site | Google Scholar
  32. M. Maskew, P. MacPhail, C. Menezes, and D. Rubel, “Lost to follow up—contributing factors and challenges in South African patients on antiretroviral therapy,” South African Medical Journal, vol. 97, no. 9, pp. 853–857, 2007. View at: Google Scholar
  33. N. C. Ware, J. Idoko, S. Kaaya et al., “Explaining adherence success in sub-Saharan Africa: an ethnographic study,” PLoS Medicine, vol. 6, no. 1, Article ID e1000011, pp. 0039–0047, 2009. View at: Publisher Site | Google Scholar
  34. E. H. Geng, D. Nash, A. Kambugu et al., “Retention in care among HIV-infected patients in resource-limited settings: emerging insights and new directions,” Current HIV/AIDS Reports, vol. 7, no. 4, pp. 234–244, 2010. View at: Publisher Site | Google Scholar
  35. B. Amuron, G. Namara, J. Birungi et al., “Mortality and loss-to-follow-up during the pre-treatment period in an antiretroviral therapy programme under normal health service conditions in Uganda,” BMC Public Health, vol. 9, article 290, 2009. View at: Publisher Site | Google Scholar
  36. T. Togun, I. Peterson, S. Jaffar et al., “Pre-treatment mortality and loss-to-follow-up in HIV-1, HIV-2 and HIV-1/HIV-2 dually infected patients eligible for antiretroviral therapy in The Gambia, West Africa,” AIDS Research and Therapy, vol. 8, no. 1, p. 24, 2011. View at: Publisher Site | Google Scholar

Copyright © 2012 Jialun Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2347 Views | 801 Downloads | 19 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder
 Sign up for content alertsSign up