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AIDS. Author manuscript; available in PMC 2023 Oct 1.
Published in final edited form as:
AIDS. 2022 Oct 1; 36(12): 1675–1682.
Published online 2022 Jul 15. doi:10.1097/QAD.0000000000003317
PMCID: PMC9444947
NIHMSID: NIHMS1818227
PMID: 35848575
Randy G. Mungwira,1 Matthew B. Laurens,2 Wongani Nyangulu,3 Titus H. Divala,1 Nginache Nampota,1 Andrea G. Buchwald,2 Osward M. Nyirenda,1 Edson Mwinjiwa,3 Maxwell Kanjala,1 Lufina Tsirizani Galileya,1 Dominique E. Earland,2 Matthew Adams,2 Christopher V. Plowe,2 Terrie E. Taylor,4 Jane Mallewa,5 Joep J. van Oosterhout,3,* and Miriam K. Laufer2, TSCQ Study Team
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Abstract
Objective:
Many individuals living with the human immunodeficiency virus (HIV) infection and receiving antiretroviral therapy (ART) reside in areas at high risk for malaria but how malaria affects clinical outcomes is not well described in this population. We evaluated the burden of malaria infection and clinical malaria, and impact on HIV viral load and CD4 count among adults on ART.
Design
We recruited Malawian adults on ART who had an undetectable viral load and ≥250 CD4 cells/mm3, to participate in this randomized trial to continue daily trimethoprim-sulfamethoxazole (TS), discontinue daily co-trimoxazole, or switch to weekly chloroquine (CQ).
Methods
We defined clinical malaria as symptoms consistent with malaria and positive blood smear, and malaria infection as Plasmodium falciparum DNA detected from dried blood spots (collected every 4–12 weeks). CD4 cell count and viral load were measured every 24 weeks. We used Poisson regression and survival analysis to compare the incidence of malaria infection and clinical malaria. Clinicaltrials.govNCT01650558.
Results
Among 1,499 participants enrolled, clinical malaria incidence was 21.4/100 person-years of observation (PYO), 2.4 /100 PYO and 1.9/100 PYO in the no prophylaxis, TS, and CQ arms, respectively. We identified twelve cases of malaria that led to hospitalization and all individuals recovered. The preventive effect of staying on prophylaxis was approximately 90% compared to no prophylaxis (TS – IRR 0.11, 95% CI 0.08, 0.15 and CQ - IRR 0.09, 95% CI 0.06, 0.13). P. falciparum infection prevalence among all visits was 187/1,475 (12.7%), 48/1,563 (3.1%), and 29/1,561 (1.9%) in the no prophylaxis, TS, and CQ arms, respectively. Malaria infection and clinical malaria were not associated with changes in CD4 count or viral load.
Conclusion
In clinically stable adults living with HIV on ART, clinical malaria was common after chemoprophylaxis stopped. However, neither malaria infection nor clinical illness appeared to affect HIV disease progression.
Introduction
Human immunodeficiency virus (HIV) and malaria are endemic in sub-Saharan Africa, increasing the risk of coinfection (1, 2). In 2019, the World Health Organization (WHO) estimated that 38 million people were living with HIV and that over 228 million malaria cases occur globally each year, mostly in sub-Saharan Africa (3, 4).
Adults residing in malaria-endemic regions have naturally acquired immunity to malaria through lifetime exposure to infection(5). The impact of HIV-associated immunosuppression on Plasmodium falciparum infection and malaria disease remains incompletely characterized. We and others have previously shown only a modest increase in rates of uncomplicated clinical malaria, particularly in high malaria burden settings (7, 8). However, a recent meta-analysis suggested increased rates of severe malaria among people living with HIV (PLHIV), compared to uninfected populations in sub-Saharan Africa(11).
In addition to the burden of malaria as an acute illness, it may impact HIV disease progression among PLHIV, which would have significant public health implications. Early studies suggested that clinical malaria episodes were associated with transient increases in viral load persisting up to nine weeks (12, 13), and decreases in CD4 count (14–16), raising the possibility that repeated malaria episodes could accelerate HIV disease. The majority of these observations were made prior to the widespread availability of antiretroviral therapy (ART) in sub-Saharan Africa. Because uptake and coverage of prophylaxis with trimethoprim-sulfamethoxazole (TS), a drug that effectively prevents malaria, are high among PLHIV in sub-Saharan Africa, the impact of malaria infection and disease on HIV severity has been challenging to characterize. In addition, the importance of malaria prevention to the overall health of PLHIV had not been rigorously evaluated in the context of widespread ART.
We conducted a clinical trial evaluating the impact of TS prophylaxis vs. chloroquine (CQ) prophylaxis vs. no prophylaxis on overall morbidity and mortality caused by malaria and HIV-associated opportunistic infections and on HIV disease progression in Malawi. The primary results showed that TS and CQ effectively prevented malaria, as described previously (17), and consistent with findings in other high malaria burden settings (18–20). Unlike previous studies that described the burden of malaria with discontinuation of cotrimoxazole prophylaxis in HIV infected populations over a short period, the two to five year follow up of the main clinical trial provided us an opportunity to evaluate the impact of malaria infection and malaria clinical disease on HIV disease progression in this population, through routine monitoring of viral load and CD4 count as well as detailed monitoring for morbidity and mortality.
Methods
STUDY POPULATION
We conducted a randomized, controlled, open-label, phase III clinical trial in adults living with HIV from two health facilities in southern Malawi as described previously (11, 14). Adults with HIV infection were enrolled if they had 1) been on ART for at least 6 months, 2) HIV viral load <400 copies/mL and, 3) a CD4 count of ≥250 cells/mm3. Participants were randomized in a 1:1:1 ratio to one of three arms: to continue standard of care with TS prophylaxis, to discontinue TS prophylaxis and begin weekly CQ, or to discontinue TS prophylaxis (no prophylaxis). Participants were followed up every 4 – 12 weeks and when ill. Dried blood spots were collected on 3M Whatman filter paper for malaria detection. Viral load and CD4 count measurements were done every 24 weeks and at study termination. CD4 count was measured using Becton-Dickinson FacsCount, and viral load was measured using Abbot RealTime HIV-1 Viral Load Assay. Participants were counselled to attend the clinic any time they were ill. For participants with symptoms of malaria including objective fever (measured temperature ≥ 37.5 °C) or reported fever within the past 48 hours and/or unexplained symptoms including nausea, vomiting and diarrhea, headache, myalgia or chills, a thick blood smear was obtained for malaria parasite detection.
The study was conducted in two sites in southern Malawi: Ndirande Health Center, in a peri-urban township on the outskirts of Blantyre, Malawi, and Zomba Central Hospital in Zomba district, a more rural setting approximately 70 kilometers from Blantyre. The two sites have different malaria burdens: malaria parasite prevalence in children under five years old is 4% in Ndirande versus 28% in Zomba (21).
MALARIA INFECTION AND CLINCAL DISEASE DETECTION
Clinical malaria was defined as any symptom of malaria and the presence of any Plasmodium falciparum parasites detected on thick blood film. Malaria was treated according to Malawi standard treatment guidelines with artemether-lumefantrine. A new clinical malaria episode was diagnosed if it occurred ≥28 days after the previous episode. Severe malaria was defined as clinical malaria resulting in hospitalization.
To determine asymptomatic prevalence of P. falciparum, all dried blood spots from Zomba site participants underwent extraction and up to eight longitudinal samples from each study participant were pooled by mixing equal volumes of nucleic acid from each study timepoint across the follow-up period for ultrasensitive PCR detection (22). Timepoints excluded from pooled analysis were enrollment, symptomatic illness (non-malaria), symptomatic illness (clinical malaria) and collections within 28 days after malaria illness. Samples from pools with detectable P. falciparum nucleic acids were then individually assessed for positivity. PCR was not done to identify asymptomatic infections at Ndirande due to low malaria prevalence.
STATISTICAL ANALYSES
Two analyses were done to examine the effect of randomized treatment arm on clinical malaria. An intention to treat (ITT) analysis included all follow-up time allocated according to participants’ randomized arm. A per-protocol (PP) analysis censored participant follow up time when any qualifying event that led to discontinuation of randomized treatment arm, including a WHO clinical stage 3 or 4 illness, a sustained decline in CD4 count below 200 cells/mm3 for more than 1 month, ART failure (defined as VL >1,000 copies/mL, confirmed after a 3-month interval with good adherence to ART), or pregnancy. The effect of randomized treatment arm was also examined by site, and we evaluated effect modification of randomized treatment arm by study site by incorporating an interaction term into regression models. Time from randomization to first malaria episode was assessed using Kaplan Meier survival analysis and Cox Proportional Hazards modeling. Incidence of clinical malaria was compared by randomized treatment arm and by study site. Incidence rate was calculated per 100 person years, and incidence rate ratios (IRR) and 95% confidence intervals were estimated using Poisson regression. Given 500 participants per arm, and assuming a baseline incidence rate of 20 infections/100 person years (18), we should have ~88% power to detect a difference of 67% or greater comparing the TS group to the no prophylaxis group. P. falciparum infection prevalence was calculated as the proportion of regularly scheduled 4 – 12 weekly visits positive for P. falciparum and we compared prevalence ratios between randomized treatment arms using Chi-squared tests.
To examine the impact of P. falciparum clinical malaria and infection on CD4 count, we estimated the change in CD4 count between measurements occurring within 250 days of each other which was the schedule for routine testing (six months +/−10 weeks). We examined the association between change in CD4 count and either P. falciparum clinical malaria or infection occurring between CD4 counts, looking at CD4 count change as both a continuous and categorical variable. We compared the distribution of CD4 count change using Wilcoxon rank-sum tests. To account for repeated measures, we examined the association between CD4 count change and preceding malaria episode using a linear mixed effect model, with a random effect for individual. For categorical analysis, we categorized CD4 change into three groups based on quartiles: decreased by >70 cells/mm3 (bottom quartile), changed ≤70 cells/mm3 (middle 50%), or increased by >70 cells/mm3 (top quartile). We additionally looked at extreme changes (either an increase or decrease of >200 cells/mm3). Categories of CD4 change were compared between visits that either were or were not preceded by clinical malaria or P. falciparum infection using Chi-squared tests.
Viral load was categorized into detectable (≥400 copies/mL) vs. undetectable (<400 copies/mL) for all analyses. The association between detectable viral load and either clinical malaria or P. falciparum infection was assessed by comparing the proportion of individuals ever having detectable viral load with and without clinical malaria, using Chi-squared tests. Low outcome numbers (few detectable viral load measurements) prevented us from accounting for repeated measures in this analysis. Analogous analyses were conducted for the association with P. falciparum infection for participants from the Zomba site.
Ethical considerations:
The study was approved by the College of Medicine Research Ethics Committee and the University of Maryland Baltimore Institutional Review Board. All participants provided written informed consent before participation in the study. ClinicalTrials.gov identifier: NCT01650558.
Role of funding source
The U. S. National Institutes of Health sponsored this study and had no role in the collection, analysis and interpretation of the data; in the writing of the report; and in the decision to submit the paper for publication.
Results
Enrolment characteristics and clinical malaria
From December 2012 to August 2016, we enrolled 1499 participants. Population characteristics are shown in Table 1. Follow-up time ranged from 3 days to 5.5 years, with most follow up time accrued at Ndirande (Supplemental Digital Content 1, Distribution of total follow up time by study arm and site). In total, 425 clinical malaria episodes were included in the ITT analysis. These events occurred in 193 individuals, with 75 having more than one event. Twelve participants were hospitalized with malaria, all in the no treatment arm. No malaria-related deaths occurred.
Table 1 –
Clinical characteristics at enrollment and by malaria status during follow up
| Characteristics at enrollment | Number enrolled (n = 1,499) | Ndirande (n= 1,099) | Zomba (n= 400) | No Clinical Malaria (n = 1,306) | Developed Clinical Malaria (n = 193) |
|---|---|---|---|---|---|
| Study Site | |||||
| Zomba, N (%) | 400 (26.7) | - | - | 306 (23.4) | 94 (48.7) |
| Ndirande, N (%) | 1,099 (73.3) | - | - | 1,000 (76.6) | 99 (51.3) |
| Age, mean (SD) | 39.1 (9.7) | 37.7 (9.6) | 42.8 (9.3) | 39.0 (9.4) | 40.7 (10.0) |
| Male, N (%) | 364 (24.3) | 262 (23.8) | 102 (25.5) | 322 (24.7) | 42 (21.8) |
| CD4 cells/mm3, median (IQR) | 522 (390, 700) | 500 (370, 693) | 564 (453, 724) | 516 (387, 695) | 577 (430, 756) |
| Hb g/dL, mean (SD) | 13.2 (1.6) | 13.2 (1.6) | 13.2 (1.6) | 13.2 (1.6) | 13.1 (1.7) |
| Years on ART, median (IQR) | 3.3 (1.5, 5.9) | 2.5 (1.2, 4.7) | 5.8 (3.9, 8.0) | 3.2 (1.4, 5.9) | 4.4 (2.0, 6.0) |
| Bed net use | |||||
| Never, N (%) | 414 (27.6) | 323 (29.4) | 91 (22.8) | 362 (27.7) | 52 (26.9) |
| Last night, N (%) | 1,043 (70) | 737 (67.1) | 306 (76.5) | 905 (69.3) | 138 (71.5) |
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IQR: Interquartile range, SD: Standard deviation
Clinical malaria
Among study participants, 193 individuals developed clinical malaria (Table 1). Using the ITT population, remaining on prophylaxis was associated with around 90% reduction in the incidence rate of clinical malaria with no significant difference between CQ and TS prophylaxis (p = 0.35) (Table 2). The results were similar when analyzing the per-protocol population. Eighty-nine percent of clinical malaria episodes could have been prevented by remaining on TS or CQ prophylaxis. Clinical malaria incidence was significantly higher in Zomba than in Ndirande in both analyses (Table 2, Supplemental Digital Content 2, Clinical malaria incidence rate per 100 person years, per-protocol analysis), however the effect of the randomized treatment arm was similar between study sites without statistically significant effect modification by study site.
Table 2.
Incidence Rate of Clinical Malaria per 100 person years
| Population (N) | N Cases | Follow up years | Incidence Rate/ 100 person years | IRR (95% CI) |
|---|---|---|---|---|
| Total (1499) | 425 | 4,956 | 8.58 | - |
| CQ (500) | 31 | 1,645 | 1.88 | 0.09 (0.06, 0.13) |
| TS (500) | 39 | 1,654 | 2.36 | 0.11 (0.08, 0.15) |
| No prophylaxis (499) | 355 | 1,657 | 21.43 | REF |
| Ndirande (1099) | 140 | 4,134 | 3.39 | 0.10 (0.08, 0.12) |
| Zomba (400) | 285 | 822 | 34.68 | REF |
| Ndirande | ||||
| Population | N Cases | Follow up years | Incidence Rate/ 100 person years | IRR (95% CI) |
| CQ (366) | 14 | 1,371 | 1.02 | 0.13 (0.07, 0.23) |
| TS (366) | 17 | 1,379 | 1.23 | 0.16 (0.09, 0.26) |
| No prophylaxis (367) | 109 | 1,384 | 7.88 | REF |
| Zomba | ||||
| Population | N Cases | Follow up years | Incidence Rate/ 100 person years | IRR (95% CI) |
| CQ (134) | 17 | 275 | 6.18 | 0.07 (0.04, 0.11) |
| TS (134) | 22 | 274 | 8.02 | 0.09 (0.06, 0.14) |
| No prophylaxis (132) | 246 | 273 | 90.21 | REF |
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At both sites, the no prophylaxis group had significantly shorter time to first clinical malaria episode (p < 0.001) compared with the CQ and TS arms. However, the difference was greater in Zomba compared with Ndirande (Supplemental Digital Content 3, Kaplan Meier survival curves for time to clinical malaria by site).
P. falciparum infection
Among 5,141 routine visits at Zomba with samples taken for malaria infection analysis, excluding enrollment and samples within 28 days of a documented illness, 264/4,595 (5.75%) of evaluable samples were positive for P. falciparum infection.
Positive samples were significantly more common in the no treatment arm (12.7%) compared with the TS (3.1%) and the CQ (1.9%) arms (p<0.001 combining both treatment arms) and the CQ arm had significantly fewer positive samples than the TS arm (p=0.02, Table 3).
Table 3.
Total P. falciparum infection prevalence in Zomba by study arm
| Study Arm (N = 400 participants) | Samples Positive/Total Samples tested for P. falciparum | Individuals Ever Positive N = 113 (28.3%) |
|---|---|---|
| CQ (n = 134) | 29/1561 (1.9%) | 15 (11.2%) |
| TS (n = 134) | 48/1563 (3.1%) | 27 (20.1%) |
| No prophylaxis (n = 132) | 187/1475 (12.7%) | 71 (53.8%) |
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Impact of clinical malaria and P. falciparum infection on CD4 count
Across both sites, 10,872 follow up visits had a CD4 count recorded at the visit. CD4 counts that occurred within 250 days of a previous visit with a CD4 count were available for 10,657 visits and were included in the analysis. The mean time between CD4 measurements was 164 days (SD = 29 days). In general, CD4 cell counts did not change significantly between study visits (mean change [SD] between visits +2.4 cells/mm3).
We recorded 326 clinical malaria episodes with CD4 cell counts before and after the event that were separated by 250 days or less. Clinical malaria was not associated with decrease in CD4 count of >70 cells/mm3 or ≥200 cells/mm3 (p >0.36 and p >0.32, respectively) (Table 4). This did not vary by study site. Limiting the analysis to clinical malaria episodes occurring 90 days or less before the second CD4 measurement showed a similar result.
Table 4.
Change in CD4 count between measurements in relation to clinical malaria episodes
| CD4 decreased by more than 70 cells/mm3 | No change in CD4 cell count (≤ 70 cells/mm3 change) | CD4 increased by more than 70 cells/mm3 | CD4 decreased by more than 200 cells/mm3 | No change in CD4 cell count (≤ 200 cells/mm3 change) | CD4 increased by more than 200 cells/mm3 | Wilcoxon p-value* | |
|---|---|---|---|---|---|---|---|
| CD4 count change when clinical malaria occurred anytime within 250 days before second measurement (n=326) | 88 (27%) | 135 (41%) | 103 (32%) | 29 (9%) | 261 (80%) | 36 (11%) | 0.36 |
| Change in CD4 count when clinical malaria occurred within 90 days before second measurement (n=222) | 59 (27%) | 87 (39%) | 76 (34%) | 23 (10%) | 179 (81%) | 20 (9%) | 0.34 |
| Change in CD4 count when no clinical malaria between measurements (n=10,331) | 2,492 (24%) | 5,143 (50%) | 2,696 (26%) | 595 (6%) | 9,163 (89%) | 573 (6%) | REF |
| Total (N=10,657) | 2,580 (24%) | 5,278 (50%) | 2,799 (26%) |
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*Wilcoxon p-value comparing the distribution of continuous change in CD4 count between observations with and without clinical malaria.
At the Zomba site, 1,819 visits with P. falciparum infection were assessed that had CD4 counts recorded within the previous 250 days. Among these, 284 (15.6%) occurred between CD4 count measurements. Preceding P. falciparum infection was not associated with a change in CD4 count of >70 cells/mm3 or >200 cells/mm3 between measurements taken < 250 days before a clinical malaria episode (global p >0.41 and p >0.88, respectively).
Among 10,491 visits with viral load measured, 90 individuals had 209 visits with detectable viral load (median viral load = 2,065 copies/mL, IQR = 983 – 10,884), with 155 (74%) having viral loads >1,000 copies/mL, a threshold for assessing virological failure (23).
Fourteen individuals in the study had coincidental clinical malaria and a detectable viral load over the study follow-up period (Supplemental Digital Content 4, Distribution of visits for 14 study participants who had both clinical malaria and detectable viral load over the study follow up period). Due to the small number of individuals with detectable viral load, we were unable to analyse whether clinical malaria was associated with detectable viral load. We found no statistically significant association between ever having clinical malaria and ever having detectable viral load. Similarly, ever having malaria infection was not associated with ever having detectable viral load (Table 5).
Table 5.
Association between ever having clinical malaria and ever having detectable viral load
| Total | Detectable viral load measurements at any time | No detectable viral load measurements | Chi-squared p-value | |
|---|---|---|---|---|
| Clinical malaria | ||||
| Total Population | 1499 | 90 (6.0%) | 1409 (94.0%) | |
| Any clinical malaria | 193 | 14 (7.3%) | 179 (92.7%) | 0.43 |
| No Clinical Malaria | 1306 | 76 (5.8%) | 1230 (94.2%) | |
| Ndirande | 1099 | 81 (7.4%) | 1018 (92.6%) | |
| Any Clinical Malaria | 99 | 10 (10.1%) | 89 (89.9%) | 0.27 |
| No Clinical Malaria | 1000 | 71 (7.1%) | 929 (92.9%) | |
| Zomba | 400 | 9 (2.2%) | 391 (97.8%) | |
| Any Clinical Malaria | 94 | 4 (4.3%) | 90 (95.7%) | 0.13 |
| No Clinical Malaria | 306 | 5 (1.6%) | 301 (98.4%) | |
| Pf Infection | ||||
| Any Pf Infection | 113 | 5 (4.4%) | 108 (95.6%) | 0.07 |
| No Pf Infection | 287 | 4 (1.4%) | 283 (98.6%) | |
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Pf, Plasmodium falciparum
Discussion
In the era of high ART coverage, malaria infection and disease remain common problems for adults living with HIV infection, especially in rural settings where malaria transmission is often higher than in urban areas. However, to our knowledge this is the first evaluation of the impact of these acute illness episodes and untreated malaria infections have on HIV disease. Our results suggest that they have no impact on HIV disease progression, as measured by HIV viral load and CD4 count, in the context of highly effective ART. Malaria endemic countries have varying policies regarding TS prophylaxis among people living with HIV infection (24). This study provides essential information about the short and long term risks, benefits and potential costs of continuing vs. discontinuing prophylaxis. Chemoprophylaxis with TS or CQ among adults on ART prevented approximately 90% clinical malaria episodes in both high and low transmission settings. The slightly better protective effect of CQ against P. falciparum infection reflects the high prevalence of CQ-susceptible malaria parasites in Malawi (25–28). Despite widespread anti-folate resistance found among the parasites in the region (25, 29), TS was highly efficacious in preventing malaria.
The high burden of clinical malaria that we observed in the control arm after discontinuation of prophylaxis was similar to findings in three other studies of discontinuation of TS in African adults who were stable on ART. Authors ascribed the increased risk of malaria to HIV infection toimmunosuppression in this population despite participants being clinically stable and on ART (18–20). Alternatively, the prolonged period of TS prophylaxis, with less natural exposure to patent malaria infection, may have led to loss of acquired host immunity to malaria infection and disease. Further investigations will be required to assess these possibilities. The high malaria prevalence in adults off TS prophylaxis is similar to in children less than five years of age in Malawi (21). In Malawi, we and others have observed similar rates of malaria infection among young children and adults (30–32), although incidence of clinical disease decreases with age (33). However, unlike young children who suffer from severe morbidity and mortality due to malaria, malaria disease in PLHIV remains generally uncomplicated and without long-term consequences.
With the highly overlapping distribution of HIV infection and malaria throughout sub-Saharan Africa, any impact of P. falciparum infection and clinical malaria on HIV disease progression would translate into relevant public health implications. We found no consistent association between malaria infection or disease and changes in CD4 count or viral load. Previous data detected increases in viral load and drops in CD4 counts after malaria episodes in adults with untreated HIV infection (12, 13, 15, 16). Our study suggests that this phenomenon does not occur when HIV infection is well controlled with ART.
Study participants received more comprehensive and higher quality care than would be routinely provided in Malawi, which may limit the generalizability of our findings. We monitored participants carefully for ART adherence, virological failure and intercurrent illnesses. We provided intensive adherence counselling, adjusted ART regimens when indicated, and promptly provided treatment for acute and chronic comorbidities in accordance with national guidelines. The observed rate of clinical malaria may have been higher than in routine clinical settings, as study participants had a malaria test done for a wide range of symptoms. However, participants across all study arms were assessed in a standardized manner by a well-trained study team, reducing the chance of detection bias. While the collection of viral load and CD4 count measurements every six months may have failed to detect transient changes in these values associated with acute infection or disease, we were unlikely to miss changes that would have clinically relevant impact on HIV progression.
Conclusion
In the absence of chemoprophylaxis, malaria infection and uncomplicated malaria disease are common among adults on ART in high malaria transmission settings. Our findings support the importance of continuous malaria control among PLHIV who are stable on ART in these settings, even though malaria infection and clinical disease have no detectable impact on CD4 count and viral load.
Supplementary Material
Revised Manuscript (All Manuscript Text Pages in MS Word format with Author and Affiliation Information)_2
1. Table: Distribution of total follow up time by study arm and site
2. Table: Clinical malaria incidence rate per 100 person years, per-protocol analysis
Click here to view.(16K, docx)
Supplemental files 3-4
3. Figure: Kaplan Meier survival curves for time to clinical malaria by site
4. Figure: Distribution of visits for 14 study participants who had both clinical malaria and detectable viral load over the study follow up period
Click here to view.(531K, docx)
Acknowledgements:
The TSCQ study team includes Felix Mkandawire, Victoria Mapemba, Maxwell Kanjala, Rhoda Masonga, Leonard Mughogho, Matthews Mwanamanga, Lameck Khonde, Dalitso Taulo, Tamandani Chimbalanga, Andrew Sigoloti, Esther Mwagomba, Jean Maloya, Barbara Katutula, Meraby Funsani, Francis Muwalo, Alinane Malere, Eva Huwa, Loyce Kantwela, Madalitso Kamoyo, Peter Majoni, Joseph Kanyangalika, Innocent Masuli.
Funding:
This study was funded to MKL by NIH U01AI089342 with additional support from K24AI114996 and R25GM113262.
National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.
Footnotes
Conflict of Interest:
No authors have conflicts of interest to declare.
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