Share this article on:

Determinants of Survival Without Antiretroviral Therapy After Infancy in HIV-1-Infected Zambian Children in the CHAP Trial

Walker, A. Sarah PhD, MSc*; Mulenga, Veronica MD†; Sinyinza, Frederick MD†; Lishimpi, Kennedy MD†; Nunn, Andrew MSc*; Chintu, Chifumbe MD, FRCPC, FRCP†; Gibb, Diana M. MbChB, MD, MSc, FRCPCH*; CHAP Trial Team

JAIDS Journal of Acquired Immune Deficiency Syndromes: 15 August 2006 - Volume 42 - Issue 5 - pp 637-645
doi: 10.1097/01.qai.0000226334.34717.dc
Epidemiology and Social Science

Background: There are few data on predictors of HIV progression in untreated children in resource-limited settings.

Methods: Children with HIV Antibiotic Prophylaxis (CHAP) was a randomized trial comparing cotrimoxazole prophylaxis with placebo in HIV-infected Zambian children. The prognostic value of baseline characteristics was investigated using Cox models.

Results: Five hundred fourteen children aged 1 to 14 (median 5.5) years contributed 607 years follow-up (maximum 2.6 years). Half were boys, and in 67%, the mother was the primary carer; at baseline, median CD4 percentage was 11% and weight was less than third percentile in 67%. One hundred sixty-five children died (27.2 per 100 years at risk; 95% confidence interval 23.3-31.6). Low weight-for-age, CD4 percentage, hemoglobin, mother as primary carer, current malnutrition, and previous hospital admissions for respiratory tract infections or recurrent severe bacterial infections were independent predictors of poorer survival, whereas oral candidiasis predicted poorer survival only when baseline CD4 percentage was not considered. Mortality rates per 100 child years of 44.5 (37.2-53.2), 14.7 (10.9-19.8), and 2.3 (0.3-16.7) were associated with new World Health Organization stages 4, 3, and 1/2, respectively, applied retrospectively; very low weight-for-age was the only staging feature for 42% of stage 4 children.

Conclusions: Malnutrition and hospitalizations for respiratory/bacterial infections predict mortality independent of immunosuppression, suggesting that they capture HIV- and non-HIV-related mortality, whereas oral candidiasis is a proxy for immunosuppression.

From the *MRC Clinical Trials Unit, London, UK; and the †University Teaching Hospital, Lusaka, Zambia, ‡A complete list of the members of the CHAP Trial Team appears at the end of this article.

Received for publication January 27, 2006; accepted April 10, 2006.

Sources of support: The CHAP trial was funded by the Department for International Development, UK.

Reprints: A. Sarah Walker, PhD, MSc, Medical Research Council Clinical Trials Unit, 222 Euston Road, London NW1 2DA, UK (e-mail:

There are few longitudinal studies describing the natural history of pediatric HIV infection in resource-limited settings. Cross-sectional clinical and postmortem studies report a wide range of diseases contributing to morbidity and death in HIV-infected African children,1-4 in particular, multiple and often coincidental infections (especially lung disease and tuberculosis), diarrheal disease, and malnutrition. As in industrialized countries, Pneumocystis jiroveci pneumonia and cytomegalovirus infections are important causes of mortality among HIV-infected infants.1-3

Small studies have reported that CD4 and, to a lesser extent, viral load predict mortality in HIV-infected children in Africa,5 as in Europe and the United States.6-8 However, these laboratory tests are not widely available in resource-limited settings. There are few African studies examining alternative measures of disease progression in the absence of antiretroviral therapy (ART), in particular, simple clinical or laboratory predictors (e.g., weight-for-age, hemoglobin) that might be more easily used to inform decisions about starting ART where more complex and costly laboratory tests are unavailable.

This question of when to start ART in children is critical given the increased ART availability in African countries,9 and the World Health Organization (WHO) is currently revising ART guidelines for children in resource-limited settings.10 In addition, WHO has proposed a new 4-stage clinical classification of pediatric HIV,11 which aims to be prognostic and meet surveillance needs. Early pediatric cohorts in Europe7 showed predictive value from hemoglobin and Centers for Disease Control and Prevention clinical category12 in addition to CD4. No such analyses have been undertaken using data from Africa. We collected current and previous clinical events and laboratory parameters at baseline in the Children with HIV Antibiotic Prophylaxis (CHAP) trial13 and explored the value of combinations of simple clinical and laboratory markers in predicting mortality in HIV-infected African children after infancy.

Back to Top | Article Outline


In the CHAP double-blind randomized controlled trial,13 HIV-antibody-positive children aged 1 to 14 years presenting to medical services at the University Teaching Hospital (UTH), Lusaka, Zambia, were randomized between March 2001 and January 2003 to receive daily cotrimoxazole prophylaxis or matching placebo. The trial was terminated early because of a 43% reduction in mortality in the cotrimoxazole group across all ages and CD4 percentage.

Information collected at baseline included demographics (age, sex, primary carer), clinical data {weight, height, prior and current signs and symptoms [lymphadenopathy, splenomegaly, hepatomegaly, oral candidiasis, malnutrition (kxwashiorkor and/or marasmus), diarrhea, severe bacterial infections, fever, developmental delay, tuberculosis, malaria, parotitis]}, and number of and reason for prior hospital admissions (validated from hospital records by trial monitors, as UTH was the main hospital for sick children living in Lusaka at this time). Laboratory data included hemoglobin and T-cell subsets. Clinical malnutrition was assessed using the WHO/Wellcome classification (weight <60% that expected for age, signs of marasmus or kwashiorkor14); tuberculosis was invariably diagnosed presumptively.

Children were assessed at randomization (trial entry) when blinded drugs were prescribed, then 4-weekly for 16 weeks and 8-weekly thereafter. Vital status after randomization was ascertained on all but one child (lost to follow-up after randomization); at trial closure, only 16 of the children not known to have died (5%) had vital status ascertained more than 4 months previously.13 ART was not readily available and was not provided during the trial. The 25 children (5%) who received ART at some stage during the trial (corresponding to only 0.9% of all follow-up time) were from families able to buy drugs themselves. Receiving ART was based more on ability to pay than clinical need.

The prognostic importance of baseline characteristics on survival was investigated using univariable and multivariable Cox proportional hazards regression. Time was measured from randomization to the earliest of date of death, first initiated ART, last seen alive, or trial closure. CD4 results were expressed as percentages of total lymphocyte counts, as these are less variable with age.15 Height, weight, and body mass index (BMI) were expressed as z scores with reference to UK standards for uninfected children.16 Where a CD4 measurement was not available within 12 weeks prior to randomization (25%, as CD4 values were only formally introduced 3 months into the trial), values up to 32 weeks before or after trial entry (but before ART) were used [similarly for hemoglobin (10% not available within 6 weeks before randomization)]. As there were a small number of missing values remaining in a few variables, 10 multiple imputations were used in multivariable models17,18 to avoid excluding different children from different analyses.

All models included age at baseline and were adjusted for allocated trial drug (checking for interactions) and age at baseline (similar results obtained stratifying by age instead). Rather than reduce power by categorizing variables with nonlinear effects,19 these were included as fractional polynomials.20,21 Multivariable models were based on backward elimination (P < 0.10) on groups of variables (e.g., demographics only, demographics plus growth). Stability of the choice of the best prognostic factors to small data perturbations was checked using bootstrap (1000 samples).22

Back to Top | Article Outline


Five hundred fourteen children aged 1 to 14 years (254 cotrimoxazole and 260 placebo) with a total 607.4 child years of follow-up (maximum 2.6 years for an individual child) were included in the analysis. Half were boys, and in 67%, the mother was the primary carer (Table 1). Most (73%) were recruited from pediatric outpatient clinics: the remainder from other clinics (8%), following inpatient admissions (7%), or referrals from other programs/studies (12%). Clinical criteria suggestive of HIV infection were used to identify children from all referral routes for antibody testing that was part of trial prescreening (i.e., children had not been tested for HIV before trial entry). Sixteen percent were more than 10 years old, and 69% had CD4 percentage <15%. Height and weight were below the third percentile (z score <−2) compared with uninfected UK children in 79% and 67%, respectively. As many were both underweight and stunted, most BMI-for-age scores were in the normal range (76% within 2 standard deviations). Seventy-four percent had experienced one or more previous hospital admissions; 37% had two or more admissions.

There was a strong association between age at trial entry and many baseline variables. Those cared for by their mother were significantly younger than those cared for by others [rank sum P < 0.0001; median (interquartile range) 3.7 years (1.9, 6.0) vs. 7.7 (4.3, 10.0), respectively]. Older children had significantly lower BMI-for-age, CD4 percentage, and total lymphocyte counts, but in contrast had significantly higher height-for-age and hemoglobin. Older children had fewer previous hospital admissions and a lower chance of having previously been malnourished or hospitalized for malnutrition, diarrhea, or respiratory infections compared with younger children, most likely due to a survival effect (all rank sum/exact P < 0.05).

There were 165 deaths (27.2 per 100 child years at risk, 95% CI 23.3-31.6) before trial closure or ART initiation (5 of 25 children died after initiating ART and are censored at ART initiation). Univariably, the strongest baseline predictors of mortality were weight-, height-, and BMI-for-age; CD4 percentage; hemoglobin; current oral candidiasis; current/prior malnutrition; and number of previous hospital admissions (all P < 0.001, Table 1). There were weaker effects of primary carer, age, and total lymphocytes, the latter 2 factors showing significant nonlinearity (P < 0.05). Prior nonspecific and/or common symptoms or signs (such as lymphadenopathy, fevers, diarrhea, and malaria) did not predict mortality (P > 0.4).

Back to Top | Article Outline

Clinical Predictors of Survival (I)

We first considered clinical variables at baseline and reasons for prior hospitalization as joint predictors of subsequent survival [Table 2(I)], grouping prior hospitalization for malnutrition (kwashiorkor and/or marasmus) and severe bacterial infections (pneumonia and/or nonrespiratory severe infections) because of small numbers. Prior oral candidiasis, current malnutrition, prior tuberculosis, and prior hospital admission for a severe bacterial or respiratory tract infection were independent clinical predictors of higher mortality (adjusted P < 0.1, Table 2). A single hospitalization for a severe bacterial infection increased the risk of death by 42%, and a further hospitalization doubled the risk again. There was a trend toward poorer survival in children whose mother was the primary carer at baseline (adjusted P = 0.07) and a highly nonlinear effect of age (Fig. 1), with children younger than 2 and older than 6 years at higher risk.

Using bootstrap methods to assess model stability, malnutrition was the most robust predictor of mortality, with all models supporting the inclusion of either current (88% of models) or past history of malnutrition (remaining 12% of models). Other predictive factors were relatively robust, being supported by 50% to 70% of models. Although there was some support (inclusion in >25% of bootstrap models) for additional independent effects of prior hepatomegaly, splenomegaly, developmental delay, hospital admission for diarrhea, and the number of prior hospital admissions, the study was not powered to detect statistically significant independent effects of all these factors.

Back to Top | Article Outline

Growth Parameters Alone as Predictors of Survival (II)

Although age-adjusted weight, height, and BMI were univariable predictors of mortality, the only independent predictor was weight-for-age [Table 2(II), 100% bootstrap support]. Effects of primary carer and age (nonlinear) remained (Fig. 1).

Back to Top | Article Outline

Laboratory Markers Alone as Predictors of Survival (III)

Baseline CD4 percentage and hemoglobin were the only independent laboratory predictors of survival [Table 2(III), both 100% bootstrap support]; as expected, there was strong confounding between CD4 percentage and total lymphocyte count.

Back to Top | Article Outline

Clinical Events and Weight as Predictors of Survival (IV)

We then considered clinical events and weight together [Table 2(IV)]. The effects of primary carer, age, weight-for-age, and prior oral candidiasis were virtually unchanged, suggesting that these predictors are independent. Of interest, children with additional features of clinical malnutrition were at higher risk of death for any given weight-for-age. After adjusting for baseline weight-for-age, few other symptoms, signs, or reasons for hospitalization (including tuberculosis or single severe bacterial infection) were predictive in main or bootstrap models, suggesting that the effect of these diseases on mortality is captured mainly through their effect on weight-for-age.

Sixty-one of the 143 children with clinical malnutrition at or before baseline had been hospitalized for malnutrition. These children (who had presumably responded to treatment in hospital) were not at higher risk of mortality compared with children of the same weight-for-age who had not previously been diagnosed with other signs/symptoms of clinical malnutrition [HR = 1.09 (95% CI 0.64-1.83) P = 0.76]. Conversely, children with clinical malnutrition but who were never admitted to a hospital remained at higher risk of death compared with children with the same weight-for-age who had not had this diagnosis previously [HR = 1.76 (1.19-2.62) P = 0.005].

Back to Top | Article Outline

Clinical and Laboratory Markers as Predictors of Survival (V)

We then considered clinical events and laboratory markers together as predictors of survival [Table 2(V)]. The effects of primary carer, CD4 percentage, hemoglobin, prior malnutrition, and hospitalizations remained similar, suggesting that these predictors are independent and, specifically, that the effects of malnutrition and prior respiratory tract or recurrent severe bacterial infections are not directly or completely mediated through immunosuppression. Conversely, the effect of oral candidiasis completely disappeared after adjusting for CD4 percentage and hemoglobin (HR = 1.09, P = 0.59).

Back to Top | Article Outline

Overall Predictors of Survival (VI)

Considering all baseline factors together, mother as primary carer, low weight-for-age, low CD4 percentage, low hemoglobin, current malnutrition, and previous hospital admissions for 2 or more severe bacterial infections or other respiratory tract infections all independently predicted higher risk of death [Table 2(VI)]. Oral candidiasis only predicted survival when baseline CD4 percentage was not included, suggesting that it is a proxy for immunosuppression and thus mortality risk. None of the other variables univariably significant at P < 0.20 in Table 1 added important predictive information (P > 0.2). As indicated earlier (model IV), prior tuberculosis diagnosis or a single hospital admission for a severe bacterial infection did not independently predict higher risk of mortality after adjusting for current weight-for-age.

Similar effects were observed in all models with or without adjusting for age. However, increased mortality risk was more pronounced in younger children after adjusting for CD4 percentage (III, V, VI) (Fig. 2). Furthermore, mortality risk in younger children was more pronounced in models that did not adjust for clinical events (models II and III), with or without adjustment for CD4 percentage. Although this may be partially due to effects of HIV not mediated through CD4 percentage and/or long-term survival bias, this also suggests that such clinical events contribute significantly to mortality in younger children, regardless of the level of immunodeficiency or low weight.

Back to Top | Article Outline

Staging of HIV Infection According to Laboratory Parameters and Weight

The strong and independent effect of weight-for-age on survival is demonstrated by survival curves categorized by CD4 percentage, weight-for-age, and hemoglobin in Figure 2A. Children with CD4 percentage <15% generally have poorer survival than those with CD4 percentage ≥15%, as expected, and low weight-for-age and/or hemoglobin makes this worse (lowest 2 curves). However, survival among children with low weight-for-age but high CD4 percentage (≥15%) actually tends to be worse than among those with normal weight-for-age and hemoglobin but low CD4 percentage (<15%).

Back to Top | Article Outline

Clinical Staging of HIV Infection

Finally, we applied the most recently defined 4-stage WHO clinical classification11 to these data. We considered current clinical malnutrition, weight-for-age less than −3, 2 or more prior hospitalizations for nonrespiratory severe bacterial infections, or Burkitt lymphoma as WHO stage 4 disease; prior oral candidiasis, tuberculosis, 2 or more hospitalizations for pneumonia or respiratory infections, hospitalization(s) for diarrhea, weight-for-age between −2 and −3, hemoglobin <8 g/dL, neutrophils <1.0 × 109/L, or platelets <50 × 109/L as WHO stage 3; or none of the above (stages 1 and 2). This classified 253 (49%), 228 (44%), and 33 (6%) children as stages 4, 3, and 1 or 2, respectively. Of these, 121 (48%), 43 (19%), and 1 (3%) died, corresponding to mortality rates per 100 child years at risk (95% CI) of 44.5 (37.2-53.2), 14.7 (10.9-19.8), and 2.3 (0.3-16.7), respectively. In addition, mortality risk was significantly higher among children classified with WHO stage 4 disease on the basis of signs of malnutrition or other clinical WHO stage 4 events with or without low weight-for-age (n = 146) compared with those with low weight-for-age alone (n = 107, P = 0.0003) (Fig. 2B).

Back to Top | Article Outline


Postmortem and cross-sectional clinical studies have emphasized the greater overlap between clinical manifestations in HIV-infected and -uninfected children in Africa than in well-resourced countries, particularly with regard to recurrent bacterial infections and malnutrition.1,4,23-25 A recent meta-analysis of birth cohorts of African HIV-infected infants born to HIV-infected mothers reported that around 50% died by age 2 years, around 7 times the mortality of uninfected children.26 Maternal death was an important risk factor in these young children, but other clinical or laboratory prognostic factors were not available. Small studies in Rwanda4 and Malawi27 reported similarly high mortality rates. All these African studies were birth cohorts with relatively short follow-up. In contrast, the median survival of HIV-infected children before effective ART in Europe and the United States is 8 to 10 years.28-30

In contrast, our prevalent study, with median age 4.4 years at entry, represents the way survivors after infancy with no previous HIV diagnosis present to medical services in many African countries. There are few longitudinal studies considering simple clinical and laboratory prognostic indicators for survival in such untreated children in resource-limited settings.31 In our study, average follow-up was nearly 2 years: low loss to follow-up (5%) is an additional benefit. Children with Pneumocystis jiroveci pneumonia or other active infections at baseline were not enrolled: very few infants were enrolled and were not included in this analysis. Those children identified during inpatient admission (7%) were recruited only after discharge from hospital. In total, one quarter had never been an inpatient, and most had received their HIV diagnosis after being referred from outpatient departments. Clinical information and laboratory measurements were obtained at baseline and supplemented by details of validated previous hospital admissions. As UTH was the main hospital for sick children living in Lusaka during the time of this study, these data are likely to be fairly complete. Although many diagnoses of previous or current illnesses were presumptive, this reflects the situation for most resource-limited settings where many HIV-infected children present late. As UTH is fairly typical of an urban poor setting, we believe our findings are generalizable to the prognosis of children in other resource-limited countries presenting to medical services with clinical symptoms/signs.

We found that malnutrition, previous bacterial infections, and oral candidiasis were the most common clinical diagnoses predicting mortality. The prognostic value of oral candidiasis disappeared after adjusting for CD4 percentage, reinforcing the point made by others that this is a good clinical proxy for low CD4 percentage.32 We found that weight-for-age was a better predictor than height-for-age or weight-for-height; weight has the considerable advantage of being easier to measure than height, especially in younger children. Of interest, weight and growth failure has been correlated with virologic response to ART in HIV-infected children in Europe and the United States.33,34

The relationships between weight-for-age and malnutrition are complex: we found prior clinical malnutrition with low current weight-for-age had a worse prognosis than low weight-for-age alone, and malnutrition predicted mortality independent of CD4 percentage. The latter is not surprising; however, the relationship is likely to be further complicated by the role of malnutrition itself in decreasing CD4 count. Recently, van Kooten Niekerk et al also reported a major effect of malnutrition on mortality in HIV-infected children in South Africa and stressed the importance of addressing access to food when assessing children for ART.35 Finally, an interesting finding in our study was that children with previous malnutrition per se were not at higher risk if they had been successfully treated during a previous hospital admission. This emphasizes the need to include response to treatment of malnutrition during evaluation of children for ART.

Eleven percent of children in this study had prior hospitalizations for multiple severe bacterial infections (mainly pneumonia) or other respiratory tract infections (bronchitis, otitis media, tonsillitis). As with malnutrition, these predicted mortality independent of CD4 percentage. Whereas a single bacterial infection or diagnosis of tuberculosis (almost all presumptive) had some predictive value when analyzed univariably, these no longer predicted mortality after adjusting for weight-for-age. This is not surprising, as wasting may be both a feature of having these diseases and a risk factor for developing them. Previous studies have also reported that survival of children with HIV infection and tuberculosis is closely related to nutritional status.36

We observed a borderline protective effect on mortality of care not being provided by the mother at baseline. Previous studies have reported that maternal death increases the child's risk of death 3-fold.26 However, a recent study suggested that although higher mortality was observed around the time of maternal death, there was no evidence that risk of death was increased among children surviving their mothers by 12 months.37 Motherless children in CHAP may have had stable alternative uninfected carers at baseline who were therefore less likely to become sick and/or die of HIV during the course of the study. We plan further analyses to assess the role of changing carers on mortality in these children.

Low hemoglobin remained a predictor of mortality in this study even after adjusting for CD4 percentage. Total lymphocyte count was highly confounded with CD4, but seemed to be a less robust predictor than suggested by a recent meta-analysis of data from untreated children in Europe and North America38 and, in particular, did not add independent information. The joint effects of total lymphocyte count and hemoglobin need evaluation in a larger data set, planned through the 3cs4kids collaboration pooling natural history data from pediatric cohort studies in resource-limited settings (

It was not possible to assign many WHO stage 4 diagnoses to the new WHO staging system11 because of difficulties in diagnosing most opportunistic infections in these children. A further limitation of this study was that symptoms and signs specific to all WHO stages were not sought prospectively, particularly for stages 1 and 2. However, despite this, we observed considerable discrimination in mortality risk by stage at baseline. The dominant contribution of low weight-for-age to stage 3 and 4 disease again emphasizes the importance of managing malnutrition before starting ART.

Back to Top | Article Outline


We thank the families and children enrolled in CHAP and other staff from the University Teaching Hospital and the School of Medicine, Lusaka, Zambia.

Back to Top | Article Outline


1. Chintu C, Mudenda V, Lucas S, et al, On behalf of the UNZA-UCLMS Project Paediatric Post-mortem Study Group. Lung diseases at necropsy in African children dying from respiratory illnesses: a descriptive necropsy study. Lancet. 2002;360:985-990.
2. Graham SM, Mtitimila EI, Kamanga HS, et al. Clinical presentation and outcome of Pneumocystis carinii pneumonia in Malawian children. Lancet. 2000;355(9201):369-373.
3. Zar HJ, Dechaboon A, Hanslo D, et al. Pneumocystis carinii pneumonia in South African children infected with human immunodeficiency virus. Pediatr Infect Dis J. 2000;19(7):603-607.
4. Spira R, Lepage P, Msellati P, et al. Natural history of human immunodeficiency virus type 1 infection in children: a five-year prospective study in Rwanda. Pediatrics. 1999;104(5):e56.
5. Taha TE, Kumwenda NI, Hoover DR, et al. Association of HIV-1 load and CD4 lymphocyte count with mortality among untreated African children over one year of age. AIDS. 2000;14(4):453-459.
6. HIV Paediatric Prognostic Markers Collaborative Study Group. Short-term risk of disease progression in HIV-1 infected children receiving no antiretroviral therapy or zidovudine monotherapy: a meta-analysis. Lancet. 2003;362:1605-1611.
7. Galli L, de Martino M, Tovo PA, et al. Predictive value of the HIV paediatric classification system for the long-term course of perinatally infected children. Int J Epidemiol. 2000;29(3):573-578.
8. Barnhart HX, Caldwell MB, Thomas P, et al. Natural history of human immunodeficiency virus disease in perinatally infected children: an analysis from the Pediatric Spectrum of Disease Project. Pediatrics. 1996;97(5):710-716.
9. WHO. Progress on global access to HIV antiretroviral therapy: an update on "3 by 5." June 2005. Geneva. Available at:
10. WHO. Scaling up antiretroviral therapy in resource-limited settings: treatment guidelines for a public health approach, 2003 revision. Geneva: 1-68. Available at:
11. WHO. Interim WHO clinical staging of HIV/AIDS and HIV/AIDS case definitions for surveillance: African region. 2005. Available at:
12. Centers for Disease Control and Prevention. 1994 revised classification system for human immunodeficiency virus infection in children less than 13 years of age. MMWR. 1994; 43 (No RR-12).
13. Chintu C, Bhat GJ, Walker AS, et al, On behalf of the CHAP Trial team. Co-trimoxazole as prophylaxis against opportunistic infections in HIV-infected Zambian children (CHAP): a double-blind randomised placebo-controlled trial. Lancet. 2004;364:1865-1871.
14. McIntosh N, Helms P, Smyth R. Forfar and Arneil's Textbook of Pediatrics, 6th ed. London: Churchill Livingstone, 2003:576-577.
15. Wade AM, Ades AE. Age related reference ranges: significance test for models and confidence intervals for centiles. Stat Med. 1994;13:2359-2367.
16. Cole TJ, Freeman JV, Preece MA. British 1990 growth reference centiles for weight, height, body mass index and head circumference fitted by maximum penalized likelihood. Stat Med. 1998;17:407-429.
17. van Buuren S, Boshuizen HC, Knook DL. Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med. 1999;18:681-694.
18. Royston P. Multiple imputation of missing values. Stata J. 2004;4:227-241.
19. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25:127-141.
20. Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. Appl Stat. 1994;43:429-467.
21. Sauerbrei W, Royston P. Building multivariable prognostic and diagnostic models: transformation of the predictors by using fractional polynomials. J R Stat Soc Ser A. 1999;162:71-94.
22. Royston P, Sauerbrei W. Stability of multivariable fractional polynomial models with selection of variables and transformations: a bootstrap investigation. Stat Med. 2003;22:639-659.
23. Chintu C, Luo C, Bhat G, et al. Impact of the human immunodeficiency virus type-1 on common pediatric illnesses in Zambia. J Trop Pediatr. 1995;41:348-353.
24. Marum LH, Tindeybwa D, Gibb DM. Care of children with HIV infection and AIDS in Africa. AIDS. 1997;(suppl 11):S125-S134.
25. Horwood C, Liebeschuetz S, Blaauw D, et al. Diagnosis of pediatric HIV infection in a primary health care setting with a clinical algorithm. Bull WHO. 2003;81:856-864.
26. Newell ML, Coovadia H, Cortina-Borja M, et al. Mortality of infected and uninfected infants born to HIV-infected mothers in Africa: a pooled analysis. Lancet. 2004;364(9441):1236-1243.
27. Taha TE, Graham SM, Kumwenda NI, et al. Morbidity among human immunodeficiency virus-1-infected and -uninfected African children. Pediatrics. 2000;106(6):e77.
28. Blanche S, Newell ML, Mayaux MJ, et al. Morbidity and mortality in European children vertically infected by HIV-1. The French Pediatric HIV Infection Study Group and European Collaborative Study. J Acquir Immune Defic Syndr. 1998;14:442-450.
29. Tovo PA, de Martino M, Gabiano C, et al. Prognostic factors and survival in children with perinatal HIV-1 infection. The Italian register for HIV infections in children. Lancet. 1992;339:1249-1253.
30. Scott GB, Hutto C, Makuch RW, et al. Survival in children with perinatally acquired human immunodeficiency virus type 1 infection. N Engl J Med. 1989;321:1791-1796.
31. Marston M, Zaba B, Salomon JA, et al. Estimating the net effect of HIV on child mortality in African populations affected by generalized HIV epidemics. J Acquir Immune Defic Syndr. 2005;38:219-227.
32. Yeung S, Wilkinson D, Escott S, et al. Paediatric HIV infection in a rural South African district hospital. J Trop Pediatr. 2000;46:107-110.
33. Paediatric European Network for Treatment of AIDS (PENTA). Comparison of dual nucleoside-analogue reverse-transcriptase inhibitor regimens with and without nelfinavir in children with HIV-1 who have not previously been treated: the PENTA 5 randomised trial. Lancet. 2002;359:733-740.
34. Lindsey JC, Hughes MD, McKinney RE, et al. Treatment-mediated changes in human immunodeficiency virus (HIV) type 1 RNA and CD4 cell counts as predictors of weight growth failure, cognitive decline, and survival in HIV-infected children. J Infect Dis. 2000;182(5):1385-1393.
35. van Kooten Niekerk NK, Knies MM, Howard J, et al. The first 5 years of the family clinic for HIV at Tygerberg Hospital: family demographics, survival of children and early impact of antiretroviral therapy. J Trop Pediatr. 2006;52:3-11.
36. Hussey GD, Reijnhart RM, Sebens AM, et al. Survival of children in Cape Town known to be vertically infected with HIV-1. S Afr J Med. 1998;88:554-558.
37. Zaba B, Whitworth J, Marston M, et al. HIV and mortality of mothers and children: evidence from cohort studies in Uganda, Tanzania, and Malawi. Epidemiology. 2005;16:275-1780.
38. HIV Paediatric Prognostic Markers Collaborative Study Group. Use of total lymphocyte count for informing when to start antiretroviral therapy in HIV-infected children: a meta-analysis of longitudinal data. Lancet. 2005;366:1868-1874.
Back to Top | Article Outline


R. Chileshe, C. Kalengo, A. Musweu Muyawa, J. Kaluwaji, M.M. Mutengo, V. Bwalya, P. Chitambala provided counseling care and follow-up for the children and their families; M. Choongo, L. Namakube, N. Kaganson, and P. Kelleher from the data entry and management team; P. Kelleher, N. Kaganson, and S. Mutambo did data monitoring. L. Farelly, N. Kaganson from the Clinical Trials Unit; J. Mwansa, D. Mwenya, K. Mutela from Microbiology; G. Mulundu, F. Kasolo from Virology; M. Yumbe from Haematology; B. Mandanda, M. Mutengo from Parasitology; V. Mudenda from Pathology; and L. Banda, T. Chipoya, B. Chanda were support staff for the CHAP team.

S. Patel and C. Ling provided microbiology support from the Royal Free and University College Medical School, London. I. Chitsike (Zimbabwe) and C. Luo (Zambia) assisted with the early development of the CHAP trial.

Data and Safety Monitoring Committee: T. Peto (Chairman), M. Sharland, M. Quigley, and G. Biemba.

Cited By:

This article has been cited 8 time(s).

JAIDS Journal of Acquired Immune Deficiency Syndromes
Differences in Factors Associated With Initial Growth, CD4, and Viral Load Responses to ART in HIV-Infected Children in Kampala, Uganda, and the United Kingdom/Ireland
Kekitiinwa, A; Lee, KJ; Walker, AS; Maganda, A; Doerholt, K; Kitaka, SB; Asiimwe, A; Judd, A; Musoke, P; Gibb, DM; on behalf of the Collaborative HIV Paediatric Study (CHIPS) Steering Committee and the Mulago Cohort Team,
JAIDS Journal of Acquired Immune Deficiency Syndromes, 49(4): 384-392.
PDF (264) | CrossRef
The cost-effectiveness of cotrimoxazole prophylaxis in HIV-infected children in Zambia
Hawkins, N; Merry, C; Barry, MG; Chintu, C; Sculpher, MJ; Gibb, DM; Ryan, M; Griffin, S; Chitah, B; Walker, AS; Mulenga, V; Kalolo, D
AIDS, 22(6): 749-757.
PDF (136) | CrossRef
Markers for predicting mortality in untreated HIV-infected children in resource-limited settings: a meta-analysis
Cross Continents Collaboration for Kids (3Cs4kids) Analysis and Writing Committee,
AIDS, 22(1): 97-105.
PDF (191) | CrossRef
Comparison of previous and present World Health Organization clinical staging criteria in HIV-infected Malawian children
Poerksen, G; Nyirenda, M; Pollock, L; Blencowe, H; Tembo, P; Chesshyre, E; Jefferis, O; Kenny, J; Moons, P; Bunn, J; Molyneux, E
AIDS, 23(14): 1913-1916.
PDF (502) | CrossRef
AIDS among older children and adolescents in Southern Africa: projecting the time course and magnitude of the epidemic
Ferrand, RA; Corbett, EL; Wood, R; Hargrove, J; Ndhlovu, CE; Cowan, FM; Gouws, E; Williams, BG
AIDS, 23(15): 2039-2046.
PDF (590) | CrossRef
The Pediatric Infectious Disease Journal
Skin Disease Among Human Immunodeficiency Virus-Infected Adolescents in Zimbabwe: A Strong Indicator of Underlying HIV Infection
Lowe, S; Ferrand, RA; Morris-Jones, R; Salisbury, J; Mangeya, N; Dimairo, M; Miller, RF; Corbett, EL
The Pediatric Infectious Disease Journal, 29(4): 346-351.
PDF (364) | CrossRef
The Pediatric Infectious Disease Journal
The Influence of Nutritional Status on the Response to HAART in HIV-Infected Children in South Africa
Naidoo, R; Rennert, W; Lung, A; Naidoo, K; McKerrow, N
The Pediatric Infectious Disease Journal, 29(6): 511-513.
PDF (429) | CrossRef
Current Opinion in Pulmonary Medicine
Community-acquired pneumonia in HIV-infected children: a global perspective
Gray, D; Zar, H
Current Opinion in Pulmonary Medicine, 16(3): 208-216.
PDF (344) | CrossRef
Back to Top | Article Outline

HIV; pediatric; natural history; Africa

© 2006 Lippincott Williams & Wilkins, Inc.