In 2011, an estimated 562,000 HIV-infected children were on antiretroviral therapy (ART) globally. Coverage of ART is estimated at 28% of children eligible for treatment when compared with 57% among adults.1 As part of the UNAIDS Global Plan to eliminate pediatric AIDS by 2015, there are calls to expand access to early infant HIV diagnosis and timely provision of ART for HIV-infected children.1 As countries and donors consider scale-up of pediatric treatment programs, there is a need for data on the cost of ART from the healthcare provider’s perspective, to inform program planning and decision analytic models for optimal allocation of resources.
Antiretroviral drugs constitute a major expenditure for HIV treatment programs, accounting for 30% to over 50% of total annual costs in low- and middle-income countries.2–6 A recent modeling study based on data from the Global Fund recipient countries projected dramatic increases in expenditures on antiretrovirals as increasing number of patients inevitably require more costly second- and third-line regimens.5 Cross-sectional studies estimated that 2%–10% of pediatric cohorts in Africa and Asia are receiving second-line therapy, whereas estimates in South America are much higher at 13%–40%, because of varying levels of program maturity and patient monitoring strategies.7,8 There are scarce data on drug utilization in children and cost implications at the program level, particularly in low- and middle-income countries.
In this study, we analyzed data from a large prospective observational cohort of children initiating ART in Thailand (NCT00433030 www.clinicaltrials.gov). The objectives were to describe the long-term trends in drug utilization and the costs from the healthcare provider perspective and to assess predictors of high drug costs.
HIV-infected children received ART as part of an open observational cohort study in a network of 28 public hospitals throughout Thailand, as described elsewhere.9,10 In brief, the study began in 1999 and is ongoing. All visits, ART and laboratory monitoring were provided free of charge. Parents/guardians provided written informed consent at study entry and from December 2006, and assent was requested from children aged 8 years and older. The study was approved by the Thai Ministry of Public Health and local Ethics Committees.
Antiretroviral naive children (<18 years old) who initiated ART (defined as ≥3 drugs including ≥2 drug class) between January 1, 1999, and January 31, 2009; and were on follow-up for >1 day were included in this analysis, with follow-up data included through 30th September, 2009.
Children initiated ART based on clinical and immunological criteria: U.S. Centers of Disease Control and Prevention (CDC) HIV clinical disease stage B or C or CD4 T-cell percentage < 20% if younger than 2 years, and CD4 < 15% if 2 years old or above.11,12 Protease inhibitor (PI)-based regimens were first available in 1999, mainly unboosted nelfinavir, and from 2006 ritonavir-boosted lopinavir. Nonnucleoside reverse transcriptase inhibitors (NNRTI)-based regimens were available from 2002, including an adult generic fixed dose combination of stavudine, lamivudine, and nevirapine (NVP; GPOvir-S) and from 2008 zidovudine, lamivudine, and NVP (GPOvir-Z) produced in Thailand. These fixed dose combinations were widely used in older children who could swallow, with tablets divided into half or quarters according to the child’s body weight.13 From 2006, there was a gradual phase out of the use of stavudine to avoid long-term toxicity.14 Children received alternative regimens as needed for toxicity/intolerance or treatment failure as defined by the physician at site.
Children attended the clinic monthly for a basic physical examination, drug refills, and adherence counseling by a nurse. They saw a physician every month during the first 3 months of treatment and every 3 months thereafter. Laboratory monitoring including CD4 and viral load (VL) assessments were conducted every 6 months.
The sites reported deaths. Loss to follow-up was defined as missed scheduled visit and no contact for over 6 months.
Data on individual treatment history was prospectively collected with start and end dates of all drugs disbursed, including dates of treatment interruptions (defined as stopping all drugs for any duration).
Treatment changes were reviewed and categorized as: (1) drug substitution: change of ≥1 drug within the same drug class; (2) switch: change of ≥1 drug including change across drug class, from NNRTI- to PI-based regimen or vice versa and (3) change to salvage therapy defined as dual PI or PI plus NNRTI-based regimen. All treatment switches were reviewed and reason for change was categorized as: treatment failure (immunologic, virologic, or clinical), toxicity/intolerance, treatment simplification (to reduce pill burden), or other reasons. Use of salvage therapy was assumed to be because of treatment failure. Two children participated in a clinical trial after entry into the observational cohort. Because their subsequent treatment did not reflect standard of care, these children were censored on entry to the trial.
The outcome of interest was drug cost, which refers only to cost of antiretrovirals and does not include cost of other medications or other related services. Costs were calculated per 6-month cycle based on individual treatment history and dosage, incorporating all treatment changes and interruptions during the cycle. Drug dosage was based on the closest weight available at start of each cycle using the WHO weight/dosage categories (<10 kg, 10–14 kg, 15–19 kg, 20–29 kg, and ≥30 kg).15 Missing weight data (5%) was imputed using the carry-forward method from the previous recorded month, except for missing baseline weight (2%), where backward imputation was used based on the next available weight. Costs were calculated up to date of death or last visit, not including the cost of prescription at last visit. Children who did not complete a 6-month cycle were censored at date of last visit or death, and drug costs were calculated up to that date. Periods of treatment interruption were assigned a cost of 0. Some drug combinations used during the initial years of the study are no longer recommended. As the aim of this analysis was not to document historical costs but rather to estimate likely costs of similar cohorts receiving treatment today, we applied the cost of the most comparable drugs or treatment combination (same drug class) widely used in Thailand today. For example, cost of unboosted nelfinavir was replaced by the cost of ritonavir-boosted lopinavir, which is the preferred PI.
Drug costs were standardized at 2009 government prices based on bulk purchasing and reported in Thai baht. Where government prices were not available, nongovernmental organization prices paid by the program were used. Costs were converted to U.S. dollar (US$) using the 2009 average market exchange rate of 34.3 baht per dollar.16
First, Kaplan–Meier probability of treatment change for failure (defined as switch across drug class for documented reason of failure or to salvage therapy) was assessed; children were at risk from date of ART initiation to first switch for failure or last follow-up visit. In addition, we assessed the probability of virologic failure defined as: nonsuppression (VL ≥400 copies) after 1 year of ART in infants or after 6 months in older children or virologic rebound with confirmed VL ≥ 400 copies after previous suppression as per Thai guidelines.17 We used the 400 copies per milliliter over the recommended 50 copies per milliliter threshold, as this was the lower limit of detection of the virologic assays during the earlier years of the program. Children were censored at date of first virologic failure or last follow-up.
Second, we assessed predictors of drug cost using the outcome of log-transformed cost per 6-month cycle. Where drug cost was 0 (because of extended treatment interruption), the value of 1 was applied to allow inclusion in the analysis. Explanatory variables considered were baseline characteristics at start of ART: sex, age, CD4%, VL, CDC stage, anthropometric measures [weight-for-age z-score (WAZ), height-for-age z-score (HAZ), and weight-for-height for age z-score based on the Thai reference curves18,19], calendar year, and initial regimen. In addition, we considered duration on follow-up and type of treatment change: drug substitution and switch across class and to salvage treatment (as separate binary variables). All explanatory variables had <10% missing values except for VL with 17% missing data.
As the outcome was repeat measures of cost over follow-up time, a multilevel regression model was used with subject level clusters.20 A random coefficient model with a random slope for follow-up time was chosen over a fixed-effect model of time, because this was a better fit to the observed data (P < 0.05). Variables associated with higher drug cost in univariable and multivariable analyses with P < 0.2 were included in the final model based on a complete case analysis. The predicted log costs were retransformed using Duan method, the mean predicted value was calculated and compared with the mean actual cycle cost, and the distribution of cluster level standardized residuals was checked.21 Results were compared with a generalized estimation equation model with an autoregressive correlation structure, using predicted individual intercepts.22 In sensitivity analyses, missing baseline explanatory variables were imputed using multiple imputation using chained equations.23 All statistical analyses were performed using STATA 11 (Stata Corporation, College Station, TX).
A total of 507 children were included in this analysis. At the start of ART, the median age was 7 years (15% ≤2 years) and the median CD4% was 7% (Table 1). Fifty-five percent of children initiated on NVP-based regimen, 40% on efavirenz (EFV)-based regimen, and 5% on PI-based regimens. As of September 2009, there were 36 deaths (7.1%), 25 loss to follow-up (4.9%), and 52 (10.1%) withdrew from the study, mostly because of relocation. The median duration of follow-up was 54 months [interquartile range (IQR), 36–72], and 207 (41%) children reached 5 years of ART.
Overall, 311 (61%) children experienced treatment change (Table 2): 187 (60%) had 1, 68 (22%) had 2, and 56(18%) had 3 or more changes. Seventy-three percent of changes were drug substitutions. Two thirds of these were changes in nucleoside reverse-transcriptase inhibitors backbone, of which 73% was stavudine replacement. If we ignored drug substitutions, 397 (78%) children would be considered to be on their initial regimen.
One hundred six children (21%) had treatment changes across drug class: 93% switched from NNRTI to PI and 7% from PI to NNRTI-based regimens. The reasons for switch were 93 (88%) treatment failure, 5 (5%) intolerance/toxicity, 3 (3%) treatment simplification, and 5 (5%) for other reasons (3 tuberculosis concomitant treatment, 1 pregnancy, and 1 by error). Among children who switched because of treatment failure, the median time to switch was 23 (IQR, 17–33) months after the start of ART. Overall, 10 children (2%) received salvage regimen at a median of 27 (IQR, 20–51) months after the start of ART. The Kaplan–Meier probability of treatment change for failure was 1.5% [95% confidence interval (CI): 0.6 to 2.9] at 1 years, 17.8% (95% CI: 14.5 to 21.7) at 3 years, and 21.4% (95% CI: 17.6 to 25.8) at 5 years of therapy (Fig. 1).
The probability of virologic failure was 18.1% (95% CI: 14.9 to 21.9), 23.4% (95% CI: 19.7 to 27.6), and 27.8% (95%CI: 23.5 to 32.5) at 1, 3, and 5 years, respectively. Among the 120 children experiencing virologic failure, 82 (68%) had a treatment change across drug class or to salvage therapy. The median duration between first virologic failure and treatment change was 9.6 months (IQR, 7.8–14.0 months).
Trends in Antiretroviral Drugs Prescribed and Costs
When we take into account all treatments disbursed, 79% was NNRTI based. However, the proportion of children on NNRTI-based regimens declined from 95% at baseline to 69% at 5 years, whereas the proportion on PI-based regimens increased from 5% to 23, respectively (see Figure S1, Supplemental Digital Content, http://links.lww.com/QAI/A430). A small proportion of children received nucleoside reverse-transcriptase inhibitor-only regimens in the process of treatment change or to address adherence issues. In addition, 44 children experienced 52 episodes of treatment interruptions, which represented 2% of total treatment time.
The mean cost of antiretrovirals varied significantly by drug class and weight category. NNRTI-based regimens were the least costly from $255 to $748 per child per year depending on weight per dosage (Table 3). PI-based regimens ranged from $582 to $1,626, whereas the most costly were dual PI salvage regimens ranging from $1047 to $2386. In this cohort, the mean cost of ARV drugs increased from $251 to $428 per child per year in the first and fifth year, respectively, with an increase of 70% (see Figure S2, Supplemental Digital Content, http://links.lww.com/QAI/A430). This corresponds with the rising number of children on PI-based regimen and to a lesser extent to salvage regimens, which accounted for 16% and 2% of all treatments disbursed and 33% and 5% of total drug costs, respectively. By year 5, PI-based and salvage regimens accounted for half of the annual drug costs (see Figure S3, Supplemental Digital Content, http://links.lww.com/QAI/A430).
Predictors of High Drug Cost
In univariable analysis, all variables were associated with the mean cycle cost except for sex, baseline VL, WAZ, weight-for-height for age z-score, and drug substitution (Table 4). In multivariable analyses, key independent predictors of high cost were older age at the start of ART, non-NVP initial regimen, switch across drug class, and receipt of salvage therapy.
Children aged 8 years or older at the start of ART had 26% higher drug cost per cycle when compared with 2–7 years olds (P < 0.0001). EFV-based and PI-based initial regimen was associated with significantly higher cycle cost when compared with NVP-based regimen (P < 0.0001). Children with a treatment switch across drug class had 45% higher cost when compared with no switch (P < 0.0001), whereas receipt of salvage regimen was associated with an 89% increase in mean drug cost when compared with children without salvage therapy (P = 0.002). In addition, high baseline HAZ and longer duration on follow-up were associated with a small increase in mean costs (P ≤ 0.006). After adjusting for these factors, baseline CD4% and CDC stage were no longer associated. These results were consistent with those obtained when using the generalized estimation equation model and the multiple imputation using chained equations dataset (data not shown). The findings were comparable when the model included only baseline characteristics and duration on ART (exclude type of treatment change), with no effect of CD4% or CDC stage, and the strongest predictor of high cost remained initial regimen (data not shown).
In this large Thai pediatric cohort, approximately two thirds of children experienced a treatment change. However, the majority of these changes were drug substitutions within the same drug class, in response to updated treatment guidelines, namely replacement of stavudine to avoid long-term toxicities.24 The Thai national pediatric treatment program with over 3400 children with a shorter median duration of follow-up of 1.7 years reported that 17.3% had a treatment change for any cause up to 2007.25 This is consistent with our observation of 61% treatment change over a median 4.5 years of follow-up, taking into account the large phase out of stavudine from 2006, which may not have been fully captured in the national program.
If we ignored drug substitutions, then 78% of children would be considered to be on their initial regimen. This is comparable with results from the United States and Europe, where 65%–71% of children remained on their initial regimen at 5 years of follow up, when allowing for drug substitutions.26,27
In this cohort, 1 in 5 children had a treatment switch across drug class, 88% of which had documented treatment failure. The probability of treatment change for failure was 21% at 5 years. This is higher than previous estimates from cross-sectional surveys, which reported only 10% of children on second therapy in Asia.7 However, the median duration on treatment in that study was unclear, and it included sites with limited access to second-line therapies and may underestimate actual need.
Furthermore, this study had routine access to VL monitoring every 6 months. Early detection of viremia allows for a more rapid switch to avoid accumulation of resistance mutations when compared with clinical or immunological only monitoring used in other settings.7 The probability of virologic failure was 23% at 3 years and 28% at 5 years of therapy. This is comparable with previous reports of virologic failure in children in Thailand.28 Among the children with virologic failure, 70% subsequently switched treatments, highlighting how some children may have remained on failing regimens despite virologic monitoring, particularly if presenting with adherence issues as reported elsewhere.29 Only 2% of children in our cohort received salvage regimen. This is lower than reports from the recent PENPACT-1 trial in the United States and Europe where 7% of children needed salvage therapy at 5 years but this was based on stringent switching criteria.27
The mean annual cost of ARV regimen per child increased by 70% from $251 to $428 over 5 years. As expected, second- and third-line regimens were key drivers of higher cost as our cohort matured. In the fifth year, one quarter of children received PI-based or salvage regimen, which accounted for half of the annual drug cost (see Figure S3, Supplemental Digital Content, http://links.lww.com/QAI/A430). Similar trends have been observed in adult studies.30 However, these higher costs of advanced regimens must be considered in the wider context. Most children respond well to second- and third-line therapies,31–33 which reduces the risk of mortality, disease progression and AIDS defining events, and potentially offsetting the cost of inpatient care in part.
It is also important to note that the highest estimated cost of drugs in children in our cohort at year 5 remains lower than the reported mean annual costs of drugs in adults in Thailand.34 A large pooled analyses of data from low- and middle-income countries estimated mean drug costs of $549 per adult per year,35 an increase from previous years because of the transition from stavudine to more costly tenofovir containing first-line regimens.
These findings have important implications: first is the importance of adherence support for children and their caregivers to maximize durability of first-line treatments. Second, improved access to affordable second- and third-line regimens is critical for the long-term sustainability of treatment programs, particularly for pediatrics programs because of the limited drug options available in child-friendly formulations.
In terms of patient-level predictors of high drug cost, there was no effect of baseline CD4% or CDC disease stage, suggesting that children initiating therapy at advanced disease stage did not incur greater costs. However, it is important to note that this refers only to cost of antiretroviral drugs and does not include cost of hospitalizations or treatment of opportunistic infections, which are known to be considerably higher in those with advanced disease stage.3,10,36 The strongest predictor of high drug cost was initiation on EFV- or PI-based initial regimens. As expected, treatment switch across drug class and to salvage regimen were also associated with higher cost. The effect of older age was most likely because of higher dosage requirement. The association between higher HAZ and not WAZ with higher drug cost was unexpected; further analyses using time-updated growth parameters may provide further insight.
There are several study limitations to consider. First, the study was based on drugs available in Thailand and prices quoted in 2009. The antiretroviral drug market is rapidly evolving with price reductions and introduction of new drugs, which may alter some of our cost estimates. For example, there have been 2 price reductions of over 5% as of May 30, 2012: lopinavir/ritonavir (100 mg/25 mg by 6.5%), saquinavir (500 mg by 12%), and the introduction of tenofovir, tenofovir/emtricitabine, and darunavir for third-line treatment. Nonetheless, our costs are still comparable with recent estimates by the WHO for low- and middle-income countries.8,37
Second, we only examined baseline prognostic variables associated with high drug cost rather than time updated variables such as current CD4, VL, or adherence, which have been reported as predictive of cost of HIV care in adults.4 Third, we did not have patient-level data on drug formulation of regimens used which may affect the cost, although we did base our estimates on the most commonly used formulation within the weight category. Fourth, we used actual cost as paid by the healthcare provider rather than market costs. Although the latter is often considered to best reflect opportunity cost of resources used,38 it would have provided an inflated estimate of cost of drugs in the Thai setting and, therefore, have limited application in informing national policies.
The development of a low-cost EFV-based fixed dose combination, which is expected to be available in Thailand at under $200 per person per year is likely to nullify the higher cost associated with EFV-based first-line regimen.39 However, we consider the other main findings to stand, particularly the higher cost associated with PI-based first-line regimen. This may have important implications as recent studies suggest that infants have superior response to PI-based initial therapy when compared with NVP-based therapy, irrespective of prior NVP exposure.40 Indeed PI-based regimens are already the preferred starting regimen for young children in resource-rich countries.26 Because PI drugs are also the basis of most second-line and salvage regimens, they are an essential component of any treatment program. Improved access to child-friendly second- and third-line drugs at affordable prices through market incentivization, price negotiations, or pooled purchasing will be critical for the long-term sustainability of national treatment programs.41,42
The authors thank the following members of the Program for HIV Prevention and Treatment (PHPT) pediatric cohort study group. Participating sites and principal investigators: Chiangrai Prachanukroh: R. Hansudewechakul, K. Preedisripipat, C. Chanta; Nakornping: S. Kanjanavanit; Prapokklao: C. Ngampiyaskul, N. Srisawasdi; Chonburi: S. Hongsiriwan; Bhumibol Adulyadej: P. Layangool, J. Mekmullica; Phayao Provincial: P. Techakunakorn, S. Sriminiphant,; Samutsakhon: P. Thanasiri, S. Krikajornkitti; Kalasin: S. Srirojana; Lamphun: P. Wannarit, K. Pagdi, R. Kosonsasitorn, R. Somsamai; Mae Chan: S. Buranabanjasatean; Sanpatong: N. Akarathum; Somdej Prapinklao: N. Kamonpakorn, M. Nantarukchaikul; Phan: S. Jungpichanvanich; Phaholpolphayuhasena: P. Attavinijtrakarn; Samutprakarn: A. Puangsombat, C. Sriwacharakarn; Rayong: W. Karnchanamayul; Buddhachinaraj: N. Lertpienthum, W. Ardong; Nakhonpathom: S. Bunjongpak; Health Promotion Region 6 Khon Kaen: S. Hanpinitsak, N. Pramukkul; Somdej Pranangchao Sirikit: T. Hinjiranandana; Mae Sai: S. Kunkongkapan; Chacheongsao: R. Kwanchaipanich; Chiang Kham: V. Wanchaitanawong, P. Jittamala; Pranangklao: P. Lucksanapisitkul, S. Watanayothin; Health Promotion Region 10 Chiang Mai: W. Jitphiankha, K. Jittayanun,; Hat Yai: B. Warachit, T. Borkird; Mahasarakam: S. Na-Rajsima, K. Kovitanggoon; Ratchaburi: C. Sutthipong, O. Bamroongshawkaseme; PHPT Clinical Trial unit Sites monitoring: P. Sukrakanchana, S. Chalermpantmetagul, C. Kanabkaew, R. Peongjakta, J. Chaiwan, Y. S. Thammajitsagul, R. Wongchai, N. Kruenual, N. Krapunpongsakul, W. Pongchaisit, T. Thimakam, R. Kaewsai, J. Wallapachai, J Thonglo, S. Jinasa, J Khanmali, P. Chart, J. Chalasin, B. Ratchanee, N. Thuenyeanyong, P. Krueduangkam, P. Thuraset, S. Thongsuwan, W. Khamjakkaew, Laboratory: P. Tungyai, J. Kamkorn, W. Pilonpongsathorn, P. Pongpunyayuen, P. Mongkolwat, L. Laomanit, N. Wangsaeng, S. Surajinda, W. Danpaiboon, Y. Taworn, D. Saeng-ai, A. Kaewbundit, A. Khanpanya, N. Boonpleum, P. Sothanapaisan, P. Punyathi, P. Khantarag, R. Dusadeepong, T. Donchai, U. Tungchittrapituk, W. Sripaoraya; PHPT Data center: S. Tanasri, S. Chailert, R. Seubmongkolchai, A. Wongja, K. Yoddee, K. Chaokasem, P. Chailert, K. Suebmongkolchai, A. Seubmongkolchai, C. Chimplee, K. Saopang, P. Chusut, S. Suekrasae, T. Yaowarat, B. Thongpunchang, T. Chitkawin, A. Lueanyod, D. Jianphinitnan, J. Inkom, N. Naratee, N. Homkham, T. Thasit, W. Wongwai, W. Chanthaweethip, R. Suaysod, T. Vorapongpisan, N. Jaisieng; Administrative support: N. Chaiboonruang, P. Pirom, T. Thaiyanant, T. Intaboonma; S. Jitharidkul, S. Jaisook, D. Punyatiam, L. Summanuch, N. Rawanchaikul, P. Palidta, S. Nupradit, T. Tankool, W. Champa; Tracking & Supplies: K. Than-in-at, M. Inta, R. Wongsang; Drug distribution center: D. Chinwong, C.Sanjoom, P. Saenchitta, P. Wimolwattanasarn, N. Mungkhala,
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