Skip Navigation LinksHome > January 2, 2010 - Volume 24 - Issue 1 > Six-month gain in weight, height, and CD4 predict subsequent...
AIDS:
doi: 10.1097/QAD.0b013e328332d5ca
Epidemiology and Social

Six-month gain in weight, height, and CD4 predict subsequent antiretroviral treatment responses in HIV-infected South African children

Yotebieng, Marcela,b; Van Rie, Anneliesa; Moultrie, Harryb; Meyers, Tammya,b

Free Access
Article Outline
Collapse Box

Author Information

aDepartment of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

bWits ECHO, Harriet Shezi Children's Clinic, Chris Hani Baragwanath Hospital, University of the Witwatersrand, Johannesburg, South Africa.

Received 27 May, 2009

Revised 2 September, 2009

Accepted 9 September, 2009

Correspondence to Marcel Yotebieng, MD, MPH, PhD, Research Assistant Professor, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA. E-mail: yotebieng@unc.edu

Collapse Box

Abstract

Objectives: Construct percentile curves for 6-month gain in weight, height, CD4 cell count, and CD4 percentage (CD4%) in children initiating ART, and to assess the association between lower percentiles and subsequent ART responses.

Design: Cohort of 1394 HIV-infected children initiating ART between April 2004 and March 2008, Johannesburg, South Africa

Methods: The generalized additive model for location, scale, and shape was used to construct percentile curves for 6-month gain in weight, height, CD4 cell count, and CD4%. Cox proportional models were used to assess the association between lower percentiles of each distribution and death, virological suppression, and treatment failure between 6 to 36 months post-ART initiation.

Results: Lower percentiles for gain in weight, CD4, and CD4% count after 6 months of ART, but not height, were associated with poor subsequent treatment outcomes independent of baseline characteristics, with increasing strength of association as percentiles decreased. Age-specific 6-month post-ART weight gain in our cohort was substantially higher compared with 6-month weight gain in non-HIV-infected American children of the Fels Institute cohort and the attained weight-for-age at 6 months post-ART plotted on WHO weight-for-age growth charts were not associated with subsequent treatment outcomes.

Conclusion: Gain in CD4% in the first 6 months of ART was the best predictor of poor subsequent ART outcomes. In areas with limited access to CD4%, weight gain post-ART using our newly developed reference distributions for HIV-infected children on ART is a good alternative to CD4%, and clearly superior to the commonly used ‘Road-to-Health’ weight-for-age charts.

Back to Top | Article Outline

Introduction

More than two million children live with HIV worldwide and more than 90% of them live in sub-Saharan Africa [1]. In the absence of ART, a third of children infected perinatally will not survive to their first birthday, and more than half will not survive their second birthday [2]. Despite recent international efforts to increase access to ART in low and middle income countries, the scaling up of ART in sub-Saharan Africa faces at least two major challenges.

First, in developed nations, the monitoring of ART consists of evaluating CD4 cell count and viral load every 3–4 months [3]. However, the measurement of viral load and CD4 cell count requires expensive and sophisticated technologies that cannot always be easily transferred or sustained in some of the poorest settings. In settings where viral load or CD4 cell count are not available, the WHO recommends that clinical parameters be used for monitoring ART, particularly the gain in weight or height in children [4].

Second, in addition to the lack of the infrastructures, sub-Saharan Africa also faces an enormous shortage of trained healthcare personnel [5]. In the context of efforts to rapidly increase access to HIV services, the WHO recommends that specific tasks be moved, where appropriate, from highly qualified health workers to health workers with shorter training and fewer qualifications [6]. For this task shifting to be efficient, simplified protocols with clear decisional algorithms must be available. Contrary to viral load, which has a clear and simple target cut-point (below detection limit), cut-points for weight, height, and CD4 cell gain that correlate with subsequent treatment outcomes have not been clearly established.

In the present study, we aimed to construct percentile curves for gain in weight, height, absolute CD4, and CD4% in the first 6 months of ART, and to test the value of the lower centile curves (3rd, 10th, 25th, 33rd, and 50th) as predictors of subsequent death, virological suppression, or treatment failure.

Back to Top | Article Outline

Methods

During the first 4 years (April 2004 to March 2008) of government-sponsored HIV treatment and care program for HIV-infected children at the Harriet Shezi Children's Clinic, a pediatric outpatient clinic at Chris Hani Baragwanath Hospital, Soweto, Johannesburg, 2193 children 15 years of age or younger were initiated on ART. Of these, 1549 (70.6%) had at least 6 months of follow-up on ART and 1394 (90.0%) with baseline and 6-month follow-up data on weight, height, absolute CD4, and CD4% were included in this analysis. ART eligibility was in accordance with national guidelines [7]. The first-line regimen included stavudine, lamivudine, and ritonavir-boosted lopinavir (LPV/r) for children 3 years of age or younger; or stavudine, lamivudine, and efavirenz for those over 3 years of age and over 10 kg of weight. Second-line regimen included zidovudine, didanosine, and nevirapine for children 3 years of age or younger; zidovudine, didanosine, and LPV/r or efavirenz for children older than 3 years of age. Children who were started on ART were clinically reevaluated at 1 month, at 3 months, and every 3 months thereafter or at any other time if clinically needed.

At each scheduled visit, children were assessed clinically and weight and height (children 2 years of age or older), or length (children <2 years) were recorded. Laboratory investigations (hematology, chemistry, viral load, and CD4 cell count) were done at baseline and every 6 months unless otherwise indicated.

Back to Top | Article Outline
Statistical analysis
Construction of percentile curves of the 6-month gain in weight, height, CD4, and CD4%

For each individual child, with n sequence data of weight, height or length, CD4 cell count, and CD4% measurements, the response curves were obtained by smoothing the n measurements over the chronological age at the time of the measurement using locally weighted quadratic regression. The 6-month estimates of weight, height, CD4, and CD4% gain were obtained by subtracting the response curve estimates at 6 months from the estimates at ART initiation [8].

To obtain the reference percentile curves, we used methods similar to those used by the WHO to construct recent international growth curves [9]. The 6-month estimates of weight, height, CD4 cell count, and CD4% gain were regressed on chronological age using the generalized additive model for location, scale, and shape, a method that requires a parametric distribution assumption for the response variable whereas allowing the modeling of the distribution parameter as nonparmetric (smooth) functions of the explanatory variables [10]. For the response variable, we assumed a Box-Cox power exponential distribution with four parameters relating to location (μ, median), scale (σ, coefficient of variation), skewness (υ, transformation for symmetry), and kurtosis (τ, power exponential parameter), respectively [11]. To specify the model, the user must choose the number of degrees of freedom (df) to be used for each parameter. Starting with the simplest model that includes age and the fitting of μ and σ curves although keeping the degree of freedom for υ and τ fixed at zero, we searched for df(μ) and then df(σ) that minimized the global deviance as indicated by the generalized Akaike information criterion (with penalty three for each degree of freedom used). In the next step, using the df(μ) and df(σ) selected in the previous, we sequentially searched for the df(υ) and df(τ) that minimized the global deviance. In the last step, Q statistic [12] and worm plots [13] were used to fine tune the selected df(μ), df(σ), df(υ), and df(τ) [11]. To facilitate the convergence of the models and obtain smoother curves [9,10,16], extreme values of 6-month gain in weight, height, absolute CD4, and CD4%, that is, values that looked far apart on visual inspection, were set to missing during the construction of the centiles curves: 22 (1.8%) observations for weight, 17 (1.4%) for height, 16 (1.5%) for CD4 cell count, and six (0.6%) for CD4%. In subsequent analyses, children with extreme values were classified as falling below the lowest or above the highest centiles.

The selected optimum set of degrees of freedom was used to fit a final model and the age-specific mean and standard deviation curves, μ(t) and σ(t), were estimated. Centile curves for selected centile τ in the interval [0, 1] were then constructed as

Equation (Uncited)
Equation (Uncited)
Image Tools

[14]. In which

Equation (Uncited)
Equation (Uncited)
Image Tools

denotes the inverse of the standard normal distribution function. These analyses were done using the generalized additive models for location scale and shape (GAMLSS) package in R [15,16]. Growth velocity is usually assessed in pediatric HIV using reference values from the Fels Institute cohort [17–20]. Estimates of the 3rd, 50th, and 90th percentile of the 6-month post-ART gain in weight and height were compared with that of a cohort of 818 non-HIV-infected and otherwise healthy white American children from the Fels Institute [21,22].

Back to Top | Article Outline
Association of lower percentiles with subsequent responses to ART

Three outcomes were considered, time to death (survival), time to first virological suppression, and time to treatment failure. For two children whose exact date of death was missing, the last visit in the clinic was used as date of death. Virological suppression was defined as the first viral load measurement below 400 HIV RNA copies/ml. Treatment failure was defined as failure to achieve virological suppression after at least 1 year of ART, failure to achieve virological suppression prior to switch to second-line regimen, or two viral load measurements above 1000 RNA copies/ml after initial viral suppression.

Kaplan Meier survival curves stratifying by the selected lower centiles (3rd, 10th, 25th, 33rd, 50th) of the 6-month weight, height, CD4, and CD4% gain and log-rank test were used to assess the association with each of the outcomes [23]. For each of the three outcomes, five Cox proportional hazard models were fitted with each of the five selected centiles as predictors. Baseline WHO clinical stage (stage I/II and III/IV); level of immunosuppression (mild or not significant, advanced, and severe according to age-specific CD4% or CD4 cell count values, or both) [4]; viral load (≥ or <5 log copies); tuberculosis (TB) treatment at ART initiation, age at ART initiation (<1.5 years, 1.5–3 years, 3–5 years, 5–8 years, and 8–15 years), and weight-for-age z score (WAZ; ≥ minus 2 SD, −2 to – 3 SD, and <−3 SD) [24,25] were included in the initial model. Using a stepwise backward selection procedure and Wald test, all covariates that did not contribute significantly to the fit of each model were dropped. The Hazard ratio (HR) and 95% confidence interval (CI) from each of the final models were reported. All variables included in the model met the proportional hazard assumption formally evaluated using the Kolmogorov-type supremum test [26].

Because attained weight-for-age and height-for-age percentile curves are commonly available and familiar to healthcare workers (Road-to-Health), to evaluate whether attained growth can be used directly to monitor ART response, we also assessed the association between the lower percentile of the attained weight-for-age and height-for-age at 6 months post-ART in our cohort and subsequent treatment outcomes. Analyses were done using SAS 9.1 (SAS Institute, Cary, North Carolina, USA). All tests were conducted using a two-sided 0.05 significance level, without correction for multiple comparisons (or uncertainty due to model selection).

Back to Top | Article Outline

Results

Description of the cohort

As of 31 March 2008, 54 (3.7%) of the 1394 children included in the analysis were loss to follow-up, 18 (1.3%) were dead, and 66 (4.7%) have been transferred out. Of the 1394 children, 699 (50.2%) were male, 872 (67.4%) in WHO clinical stage III and IV, 1120 (81.9%) were severely immunosuppressed, 772 (58.3%) had viral load more than 5 log RNA copies/ml, 378 (27.9%) had WAZ less than −3 SD, 512 (38.1%) had height-for-age z score (HAZ) less than −3 SD, 353 (25.3%) were on TB treatment, and 262 (18.8%) were 17 months or younger at the time of ART initiation (Table 1). Of the 1394 children, 1249 (89.6%) had baseline and 6 months data on weight, 1228 (88.1%) on height, 1071 (76.8%) on CD4 cell count, and 1066 (76.5%) on CD4%. The distribution of baseline characteristics did not vary by availability of follow-up data.

Table 1
Table 1
Image Tools
Back to Top | Article Outline
Six-month weight and height gain and their distribution compared with that of normal white American children of the Fels Institute cohort

In the first 6 months post-ART, the median weight gain among boys was 2.73 kg among 1-year-old, decreased to 1.41 kg among 4-year-old, after which it increased to 3.05 kg among 14-year-old (Fig. 1). Similarly, median weight gain in girls was 2.63 kg among 1-year-old, decreased to 1.37 kg among 5-year-old, and rose to 3.16 kg among 15-year-old. The median height gain decreased from 8.54 cm among 1-year-old to 1.79 cm among 12-year-old males, and from 8.58 cm among 1-year-old to a 2.18 cm among 12-year-old females (Fig. 2).

Fig. 1
Fig. 1
Image Tools
Fig. 2
Fig. 2
Image Tools

Compared with the gain observed among healthy US children (Fels Institute Cohort), the 6-month weight gain whether at the lower or upper tails of the distribution was consistently higher in our cohort of HIV-infected South African children initiating ART, and the height gain particularly in the lower tails of the distribution was substantially and consistently lower, with differences amounting to up to 2 cm for the 3rd percentile.

Back to Top | Article Outline
Six-month CD4 cell count and CD4% gain

The CD4 cell count and CD4% improved substantially after ART initiation. Although the gain in absolute CD4 cell count decreased with increasing age (from 672 cells among 1-year-old to 310 cells among 6-year-old and 154 cells among 15-year-old), the gain in CD4% remained relatively stable across ages, ranging from 9.1% among 1-year-old to 7.5% among 15-year-old (Fig. 3). Children below the 3rd percentile all had negative gains, and those below the 10th percentile barely maintained their baseline level of absolute CD4 or CD4% count (Fig. 3).

Fig. 3
Fig. 3
Image Tools
Back to Top | Article Outline
Association between the 3rd, 10th, 25th, 33rd, and 50th percentile of 6-month post-ART weight, height, and CD4 gain and survival

Of the 1394 children included in the analysis, 18 (1.3%) deaths occurred over the 2792.2 years of follow-up, corresponding to a mortality rate of 6.4 (95% CI: 4.1, 10.2) deaths per 1000 child-years between 6 months and 47 months. Children in the lower percentile of weight, CD4 cell count, and CD4% gain at 6 months of ART had statistically higher crude hazard of death than those with greater gains (Table 2). After adjustment for WHO clinical stage, WAZ, and TB treatment at ART initiation, the HRs comparing children below the 33rd percentile of weight, CD4, and CD4% gain to those above were 4.52 (95% CI: 1.47, 13.90) for weight, 3.03 (95% CI: 1.01, 9.11) for CD4 cell count, and 2.60 (95% CI: 0.87, 7.74) for CD4%. HRs increased with lower percentiles. There was no association between 6-month height gain and survival.

Table 2
Table 2
Image Tools
Back to Top | Article Outline
Association between the 3rd, 10th, 25th, 33rd, and 50th percentile of 6-month post-ART weight, height, and CD4 gain and subsequent viral suppression

Almost all children achieved virological suppression, with Kaplan Meier estimates of 84.4% by 12 months and 96.4% by 24 months. Children in the lower percentile of weight, CD4 cell count, and CD4% gain at 6 months of ART were at lower hazard of virological suppression than those at higher percentiles (Table 2). After adjustment for age (weight), or age and baseline WHO clinical stage (CD4 cell count and CD4%), the HRs for viral suppression comparing children below the 33rd percentile to those above for weight, CD4, and CD4% gain at 6 months of ART were 0.80 (95% CI: 0.70, 0.91), 0.79 (95% CI: 0.69, 0.92), and 0.73 (95% CI: 0.63, 0.84), respectively (Table 2). The HRs for viral suppression were smaller with lower percentiles. Height gain in the first 6 months of ART was not associated with virological suppression.

Back to Top | Article Outline
Association between the 3rd, 10th, 25th, 33rd, and 50th percentile of 6-month post-ART weight, height, and CD4 gain and subsequent treatment failure

The Kaplan Meier estimates of the proportion of children who failed treatment rose from 0.1% by 12 months to 5.5% by 24 months and 20.1% at 36 months. Children in the lower percentiles of the distribution of weight, CD4 cell count, and CD4% gain at 6 months of ART were at increased hazard of treatment failure. After adjustment for WHO clinical stage and severity of immunosuppression at ART initiation, the HRs for treatment failure comparing children with weight gain below the 33rd percentile to those with gain above was 1.56 (95% CI: 1.07, 2.28; Table 2). For children falling below the 33rd percentile of CD4 and CD4% gain, the HRs were 1.46 (95% CI: 1.06, 2.30) and 2.43 (95% CI: 1.66, 3.57), respectively, after adjustment for WHO clinical stage and WAZ (Table 2). As for survival, the strength of the association increased with lower percentiles.

Back to Top | Article Outline
Association between the 3rd, 10th, 25th, 33rd, and 50th percentile of attained weight and height for age (Road-to-Health chart) at 6 months post-ART and subsequent treatment outcomes

Six-month after ART, the median weight-for-age and height-for-age attained in our cohort was below the 8th (WAZ = −1.41) percentile for weight and below the 1st (HAZ = −2.37) percentile for height on the Road-to-Health chart. Children below the 3rd percentile for weight tended to be at higher risk for death [adjusted HR (aHR) 4.63; 95% CI: 0.78, 27.42], but there was no correlation between weight or height for age at 6 months of ART and virological suppression or treatment failure aHRs 0.98 (95% CI: 0.86, 1.11) and 0.74 (0.50, 1.10), respectively.

Back to Top | Article Outline

Discussion

It is well established that poor nutritional status and stunting at the time of ART initiation are correlated with poor prognosis of HIV infection in children [27,28]. Monitoring height and weight is routinely performed in the follow-up of children, but there are no reference values for weight or height gain among children receiving ART [4]. We constructed reference curves for monitoring 6 month gain in weight, height, CD4 cell count, and CD4% in children initiating ART, and demonstrated that, among children who survived the first 6 months of ART, lower percentiles of CD4% gain, weight and CD4 cell count, but not height, were associated with treatment outcomes, with increasing strength of association with decreasing percentiles.

In our cohort of South African children, 6-month weight gain was consistently and substantially higher than that observed in the Fels cohort of white American children enrolled prenatally and followed up between 1929 to 1978, whereas 6-month height gain was consistently lower across all ages and sex [21,22]. Similar differences have been observed with South African children born between April and June, 1990, (when the HIV prevalence among pregnant women was 0.7%) [29], suggesting that the differences between our cohort of HIV-infected children and healthy US children may only be due in part to the presence of HIV infection.

The Fels reference values have been used in several pediatric antiretroviral drug trials in the United States [17–20]. Our results demonstrate that using the Fels cohort as a reference for HIV-infected children in South Africa, may result in an important underestimation of the number of children at risk of poor ART outcomes. For example, a boy who initiates ART at age of 18 months and gains 1.5 kg in the first 6 months has a weight gain of the 90th percentile (+1 SD) on the Fels cohort growth velocity curves, but scores below the 10th percentile (−1 SD) on our newly developed reference curves of weight gain for HIV-infected children on ART. A close follow-up of this child would be warranted as his weight gain predicts a 6.7-fold hazard of death, 1.8-fold hazard of virologic failure and reduced likelihood (aHR 0.8) of virological suppression, compared with children with sex-specific and age-specific weight gain above the 10th percentile.

In contrast to 6-month weight gain plotted on our newly developed reference charts for HIV-infected children, weight at 6 months of ART plotted on standard weight-for-age charts, did not appear appropriate for the purpose of monitoring ART response in our cohort. In our cohort, though as expected children with weight-for-age or height-for-age below the 3rd percentile were at increased hazard for subsequent mortality, there was no correlation with subsequent virological suppression or treatment failure.

CD4% gained in the first 6 months of ART showed the strongest association with poor ART outcomes. Monitoring gains in CD4% are particularly attractive as the CD4% gain was relatively stable across ages. In our cohort, children whose CD4% gain was less than 6% after 6 months of ART fell below the 33rd percentile of CD4% gain and had a 2.6-fold increased hazard of death, 2.4-fold greater hazard of treatment failure and were less likely (aHR 0.7) to achieve viral suppression compared with children who gained 6% or more in CD4%. However, as with viral load, access to CD4% measurement is not widespread in resource poor settings, limiting its usefulness in monitoring ART response.

Our analysis has many strengths including a large sample size and a long follow-up (median follow-up time on ART of 25 months), and the use of three different outcomes limiting the impact of potential errors that might result from measurement of each. Our analysis also has a number of limitations. First, we only focus on one interval, the first 6 months of ART, chosen to match the recommended time for viral load or CD4 measurement [7] and in accordance with previous studies [27,28]. Second, most children were referred after hospitalization, with few children being referred from primary healthcare clinics, thus selecting for children that may have been sicker at ART initiation and have greater gains in weight, height, and CD4 cell count if they survive the early weeks of treatment [30]. Although children in our cohort do not appear to differ substantially at ART initiation from children in other cohorts in the region [31,32], reference curves constructed from a more representative population are needed. Third, despite ART treatment some children had negative weight, height, CD4, and CD4% gain at 6 months. Although negative gains in CD4 cell count and weight can occur, negative height or length gains are likely because of measurement error occurring under routine conditions in a busy clinic. It has been shown that because of measurement errors, a single height velocity measurement even at 12 months lacks the precision to provide a reliable index of current growth, particularly in short children [33]. Fourth, some important confounders such as adherence were not measured and could not be adjusted for.

Back to Top | Article Outline
Conclusion

We demonstrated that though weight gain is an excellent tool for monitoring the early response to ART, growth references from non-HIV-infected children do not properly discriminate HIV-infected children with inappropriate growth response to ART. Our results also suggest that even with the reference distributions we constructed, the usual −2 SD (3rd percentile) cut-off for growth failure definition misses a lot of children that are at high-risk of failing ART and that substantially higher cut-offs should be considered. Gain in CD4% in the first 6 months of ART was the best predictor of poor subsequent ART outcomes. But in the areas with limited access to viral load or CD4 measurement, weight gain post-ART using our newly developed reference distributions for HIV-infected children on ART may be a good alternative. Developing an international collaboration aimed at gathering data from ART pediatric programs in other resource-constrained settings would be of crucial interest such that international reference curves can be constructed.

Back to Top | Article Outline

Acknowledgements

M.Y. was an NIH Fogarty Fellow sponsored by the grant No. DHHS/NIH/FIC 5 D43 TW01039-08 AIDS International Training and Research Program at the University of North Carolina at Chapel Hill (UNC). He also received additional support from the UNC Center for Global Initiatives and from the American International Health Alliance. Wits Echo and Harriet Shezi children's clinic in addition to the South African government funding received support from USAID/PEPFAR. We are extremely grateful to the dedicated staff of Harriet Shezi Children's clinic for their amazing work. We thank Drs Frieda Behets, Stephen Cole, and Adaora Adimora for their helpful comments.

Back to Top | Article Outline

References

1. UNAIDS. 2007 AIDS epidemic update. 2007. pp. 1–50. http://data.unaids.org/pub/EPISlides/2007/2007_epiupdate_en.pdf. Accessed (21 September 2009).

2. Newell ML, Coovadia H, Cortina-Borja M, Rollins N, Gaillard P, Dabis F. Mortality of infected and uninfected infants born to HIV-infected mothers in Africa: a pooled analysis. Lancet 2004; 364:1236–1243.

3. Working Group on Antiretroviral Therapy and Medical Management of HIV-infected Children. Guidelines for the use of antiretroviral agents in pediatric HIV infection. 2008. pp. 1–139. http://aidsinfo.nih.gov/ContentFiles/PediatricGuidelines.pdf. Accessed (20 September 2009).

4. World Health Organization. Antiretroviral therapy of HIV infection in infants and children in resource-limited settings: towards universal access. Geneva, Switzerland: World Health Organization; 2006. pp. 1–144.

5. Kober K, Van Damme W. Scaling up access to antiretroviral treatment in southern Africa: who will do the job? Lancet 2004; 364:103–107.

6. World Health Organization. Task shifting: rational redistribution of tasks among health workforce teams, global recommendations and guidelines. 2007. pp. 1–86. http://data.unaids.org/pub/Manual/2007/ttr_taskshifting_en.pdf. Accessed (21 September 2009).

7. Meyers T, Eley B, Leoning W. Guidelines for the management of HIV-infected children. Johannesburg: Jacana Media; 2005.

8. Carey VJ, Yong FH, Frenkel LM, McKinney RM. Growth velocity assessment in paediatric AIDS: smoothing, penalized quantile regression and the definition of growth failure. Stat Med 2004; 23:509–526.

9. WHO Multicentre Growth Reference Study Group (2009). WHO child growth standards: growth velocity based on weight, length and head circumference – methods and development. Geneva, Switzerland: World Health Organization. p. 242.

10. Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. J R Stat Soc Ser C Appl Stat 2005; 54:507–554.

11. Rigby RA, Stasinopoulos DM. Smooth centile curves for skew and kurtotic data modelled using the Box-Cox power exponential distribution. Stat Med 2004; 23:3053–3076.

12. Royston P, Wright EM. Goodness-of-fit statistics for age-specific reference intervals. Stat Med 2000; 19:2943–2962.

13. van Buuren S, Fredriks M. Worm plot: a simple diagnostic device for modelling growth reference curves. Stat Med 2001; 20:1259–1277.

14. Wei Y, Pere A, Koenker R, He X. Quantile regression methods for reference growth charts. Stat Med 2006; 25:1369–1382.

15. Stasinopoulos D, Rigby R. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat Software 2007; 23:1–56.

16. Stasinopoulos D, Rigby R, Akantziliotou C. Instructions on how to use the GAMLSS package in R. 2nd ed. London: STORM Centre, London Metropolitan University; 2008. Technical report 01/06.

17. Bakshi SS, Britto P, Capparelli E, Mofenson L, Fowler MG, Rasheed S, et al. Evaluation of pharmacokinetics, safety, tolerance, and activity of combination of zalcitabine and zidovudine in stable, zidovudine-treated pediatric patients with human immunodeficiency virus infection. AIDS Clinical Trials Group Protocol 190 Team. J Infect Dis 1997; 175:1039–1050.

18. Englund JA, Baker CJ, Raskino C, McKinney RE, Petrie B, Fowler MG, et al. Zidovudine, didanosine, or both as the initial treatment for symptomatic HIV-infected children. AIDS Clinical Trials Group (ACTG) Study 152 Team. N Engl J Med 1997; 336:1704–1712.

19. Kline MW, Van Dyke RB, Lindsey JC, Gwynne M, Culnane M, McKinney RE Jr, et al. A randomized comparative trial of stavudine (d4T) versus zidovudine (ZDV, AZT) in children with human immunodeficiency virus infection. AIDS Clinical Trials Group 240 Team. Pediatrics 1998; 101:214–220.

20. McKinney RE Jr, Johnson GM, Stanley K, Yong FH, Keller A, O'Donnell KJ, et al. A randomized study of combined zidovudine-lamivudine versus didanosine monotherapy in children with symptomatic therapy-naive HIV-1 infection. The Pediatric AIDS Clinical Trials Group Protocol 300 study team. J Pediatr 1998; 133:500–508.

21. Baumgartner RN, Roche AF, Himes JH. Incremental growth tables: supplementary to previously published charts. Am J Clin Nutr 1986; 43:711–722.

22. Roche AF, Himes JH. Incremental growth charts. Am J Clin Nutr 1980; 33:2041–2052.

23. Collett D (2003). Modelling survival data in medical research. 2nd ed. Florida: Chapman&Hall/CDC.

24. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, Flegal KM, Guo SS, Wei R, et al. CDC growth charts: United States. Adv, Data 2000: 314: 1–27.

25. WHO Multicentre Growth Reference Study Group. WHO child growth standards: length/height-for-age, weight-for-age, weight-for-length, weight-for-height, and body mass index-for-age – methods and development. Geneva, Switzerland: WHO.

26. Lin DY, Wei LJ, Ying Z. Checking the Cox model with cumulative sums of martingale-based residuals. Biometrika 1993; 80:557–572.

27. Benjamin DK Jr, Miller WC, Ryder RW, Weber DJ, Walter E, McKinney RE Jr. Growth patterns reflect response to antiretroviral therapy in HIV-positive infants: potential utility in resource-poor settings. AIDS Patient Care STDS 2004; 18:35–43.

28. Carey VJ, Yong FH, Frenkel LM, McKinney RE Jr. Pediatric AIDS prognosis using somatic growth velocity. AIDS 1998; 12:1361–1369.

29. Cameron N, De Wet T, Ellison G, Bogin B. Growth in height and weight of South African urban infants from birth to five years: the Birth to Ten study. Am J Hum Biol 1998; 10:495–504.

30. Moultrie H, Yotebieng M, Kuhn L, Meyers T. Mortality and virological outcomes of 2105 HIV-infected children receiving ART in Soweto, South Africa. In: Proceedings of the 16th Conference on Retroviruses and Opportunistic Infections; 2009; Montreal, Canada.

31. Bolton-Moore C, Mubiana-Mbewe M, Cantrell RA, Chintu N, Stringer EM, Chi BH, et al. Clinical outcomes and CD4 cell response in children receiving antiretroviral therapy at primary healthcare facilities in Zambia. JAMA 2007; 298:1888–1899.

32. Sutcliffe CG, van Dijk JH, Bolton C, Persaud D, Moss WJ. Effectiveness of antiretroviral therapy among HIV-infected children in sub-Saharan Africa. Lancet Infect Dis 2008; 8:477–489.

33. Voss LD, Wilkin TJ, Bailey BJ, Betts PR. The reliability of height and height velocity in the assessment of growth (the Wessex Growth study). Arch Dis Child 1991; 66:833–837.

Keywords:

ART monitoring; CD4; children; South Africa; viral load; weight

© 2010 Lippincott Williams & Wilkins, Inc.

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.