Conference Material > Poster
Niykayo LF, Mahajan R, Sagrado MJ, Ajack YBP, Chol BT, et al.
MSF Paediatric Days 2024. 2024 May 3; DOI:10.57740/CO9XKuY
Conference Material > Poster
Sheikh Mohamed A, Ilyas A, Abbas A, Avochi S, Kihara M, et al.
MSF Paediatric Days 2024. 2024 May 3; DOI:10.57740/fDNraEM
Conference Material > Poster
Moreto-Planas L, Mahajan R, Sagrado MJ, Flevaud L, Gallo J, et al.
MSF Scientific Day International 2023. 2023 June 7; DOI:10.57740/0xmg-7p42
Journal Article > ResearchFull Text
PLOS One. 2022 July 29; Volume 17 (Issue 7); e0271910.; DOI:10.1371/journal.pone.0271910
Mesic A, Decroo T, Mar HT, Jacobs BKM, Thandar MP, et al.
PLOS One. 2022 July 29; Volume 17 (Issue 7); e0271910.; DOI:10.1371/journal.pone.0271910
INTRODUCTION
Despite HIV viral load (VL) monitoring being serial, most studies use a cross-sectional design to evaluate the virological status of a cohort. The objective of our study was to use a simplified approach to calculate viraemic-time: the proportion of follow-up time with unsuppressed VL above the limit of detection. We estimated risk factors for higher viraemic-time and whether viraemic-time predicted mortality in a second-line antiretroviral treatment (ART) cohort in Myanmar.
METHODS
We conducted a retrospective cohort analysis of people living with HIV (PLHIV) who received second-line ART for a period >6 months and who had at least two HIV VL test results between 01 January 2014 and 30 April 2018. Fractional logistic regression assessed risk factors for having higher viraemic-time and Cox proportional hazards regression assessed the association between viraemic-time and mortality. Kaplan-Meier curves were plotted to illustrate survival probability for different viraemic-time categories.
RESULTS
Among 1,352 participants, 815 (60.3%) never experienced viraemia, and 172 (12.7%), 214 (15.8%), and 80 (5.9%) participants were viraemic <20%, 20–49%, and 50–79% of their total follow-up time, respectively. Few (71; 5.3%) participants were ≥80% of their total follow-up time viraemic. The odds for having higher viraemic-time were higher among people with a history of injecting drug use (aOR 2.01, 95% CI 1.30–3.10, p = 0.002), sex workers (aOR 2.10, 95% CI 1.11–4.00, p = 0.02) and patients treated with lopinavir/ritonavir (vs. atazanavir; aOR 1.53, 95% CI 1.12–2.10, p = 0.008). Viraemic-time was strongly associated with mortality hazard among those with 50–79% and ≥80% viraemic-time (aHR 2.92, 95% CI 1.21–7.10, p = 0.02 and aHR 2.71, 95% CI 1.22–6.01, p = 0.01). This association was not observed in those with viraemic-time <50%.
CONCLUSIONS
Key populations were at risk for having a higher viraemic-time on second-line ART. Viraemic-time predicts clinical outcomes. Differentiated services should target subgroups at risk for a higher viraemic-time to control both HIV transmission and mortality.
Despite HIV viral load (VL) monitoring being serial, most studies use a cross-sectional design to evaluate the virological status of a cohort. The objective of our study was to use a simplified approach to calculate viraemic-time: the proportion of follow-up time with unsuppressed VL above the limit of detection. We estimated risk factors for higher viraemic-time and whether viraemic-time predicted mortality in a second-line antiretroviral treatment (ART) cohort in Myanmar.
METHODS
We conducted a retrospective cohort analysis of people living with HIV (PLHIV) who received second-line ART for a period >6 months and who had at least two HIV VL test results between 01 January 2014 and 30 April 2018. Fractional logistic regression assessed risk factors for having higher viraemic-time and Cox proportional hazards regression assessed the association between viraemic-time and mortality. Kaplan-Meier curves were plotted to illustrate survival probability for different viraemic-time categories.
RESULTS
Among 1,352 participants, 815 (60.3%) never experienced viraemia, and 172 (12.7%), 214 (15.8%), and 80 (5.9%) participants were viraemic <20%, 20–49%, and 50–79% of their total follow-up time, respectively. Few (71; 5.3%) participants were ≥80% of their total follow-up time viraemic. The odds for having higher viraemic-time were higher among people with a history of injecting drug use (aOR 2.01, 95% CI 1.30–3.10, p = 0.002), sex workers (aOR 2.10, 95% CI 1.11–4.00, p = 0.02) and patients treated with lopinavir/ritonavir (vs. atazanavir; aOR 1.53, 95% CI 1.12–2.10, p = 0.008). Viraemic-time was strongly associated with mortality hazard among those with 50–79% and ≥80% viraemic-time (aHR 2.92, 95% CI 1.21–7.10, p = 0.02 and aHR 2.71, 95% CI 1.22–6.01, p = 0.01). This association was not observed in those with viraemic-time <50%.
CONCLUSIONS
Key populations were at risk for having a higher viraemic-time on second-line ART. Viraemic-time predicts clinical outcomes. Differentiated services should target subgroups at risk for a higher viraemic-time to control both HIV transmission and mortality.
Journal Article > ResearchFull Text
AIDS Res Ther. 2021 April 21; Volume 18 (Issue 1); DOI:10.1186/s12981-021-00336-0
Mesic A, Spina A, Mar HT, Thit P, Decroo T, et al.
AIDS Res Ther. 2021 April 21; Volume 18 (Issue 1); DOI:10.1186/s12981-021-00336-0
Protocol > Research Study
Caleo GNC, Dada M, Gray NSB, Sangma M, Scoizzato L, et al.
2018 July 1
Specific Objectives: 1. Explain dynamics of injury risk over time by:
1.1. Describing the circumstances of incidents leading to an injury (injury risks or dynamics of incident)
1.2. Describing the circumstances of near-miss incident where no injury or illness occurs (incident risks)
1.3. Measuring frequency and severity of injuries (burden)
1.4. Describe perceptions of risks amongst owner/manager/workers
2. Design acceptable interventions to reduce injury risks
3. Document intervention feasibility by:
3.1. Describing acceptability, capturing adherence to interventions and changes in risk perceptions
3.2. Describing practicality:
3.2.1. Documenting operational challenges and lessons learned
3.2.2. Capturing resources (human resources, time, materials and cost) of implementation
4. Describe any changes in worker safety behaviour and incident incidence rate
1.1. Describing the circumstances of incidents leading to an injury (injury risks or dynamics of incident)
1.2. Describing the circumstances of near-miss incident where no injury or illness occurs (incident risks)
1.3. Measuring frequency and severity of injuries (burden)
1.4. Describe perceptions of risks amongst owner/manager/workers
2. Design acceptable interventions to reduce injury risks
3. Document intervention feasibility by:
3.1. Describing acceptability, capturing adherence to interventions and changes in risk perceptions
3.2. Describing practicality:
3.2.1. Documenting operational challenges and lessons learned
3.2.2. Capturing resources (human resources, time, materials and cost) of implementation
4. Describe any changes in worker safety behaviour and incident incidence rate
Conference Material > Poster
Cuenca P, Skidmore J, Adwok E, Dau S, Nggilari J, et al.
MSF Paediatric Days 2024. 2024 May 3; DOI:10.57740/AN1pSCil
Journal Article > ResearchFull Text
Open Forum Infect Dis. 2024 May 2; Volume 11 (Issue 5); ofae221.; DOI:10.1093/ofid/ofae221
Moretó-Planas L, Mahajan R, Fidelle Nyikayo L, Ajack YBP, Tut Chol B, et al.
Open Forum Infect Dis. 2024 May 2; Volume 11 (Issue 5); ofae221.; DOI:10.1093/ofid/ofae221
BACKGROUND
Over half of childhood tuberculosis (TB) remains undiagnosed yearly. WHO recommends Xpert-Ultra as a first paediatric diagnosis test, but microbiological confirmation remains low. We aimed to determine the diagnostic performance of Xpert-Ultra on stool and urine in presumptive paediatric TB cases in two high-TB burden settings.
METHODS
This Médecins sans Frontières cross-sectional multicentric study took place at Simão Mendes hospital, Guinea-Bissau (July 2019 to April 2020) and in Malakal hospital, South Sudan (April 2021 to June 2023). Children 6 months to 15 years with presumptive TB underwent clinical and laboratory assessment, with one respiratory and/or extrapulmonary sample (gold standard (GS)), one stool and one urine specimen analysed with Xpert-Ultra.
RESULTS
A total of 563 children were enrolled in the study, 133 from Bissau, 400 from Malakal; 30 were excluded. Confirmation of TB was achieved in 75 (14.1%) while 248 (46.5%) had unconfirmed TB. Of 553 with GS specimen, the overall diagnostic yield was 12.4% (66/533). A total of 493 and 524 samples were used to evaluate Xpert-Ultra on stool and on urine, respectively. Compared to GS, sensitivity and specificity of Xpert-Ultra on stool were 62.5%(95%CI:49.4-74) and 98.3%(95%CI:96.7-99.2), whereas on urine were 13.9%(95%CI:7.5-24.3) and 99.4%(95%CI:98.1-99.8), respectively. Nine patients were positive on stool and/or urine but negative on GS.
CONCLUSIONS
Xpert-Ultra on stool showed moderate to high sensitivity and high specificity when compared to GS and an added diagnostic yield when GS was negative. Xpert-Ultra on stool was useful in extrapulmonary cases. Xpert-Ultra in urine showed low test performance.
Over half of childhood tuberculosis (TB) remains undiagnosed yearly. WHO recommends Xpert-Ultra as a first paediatric diagnosis test, but microbiological confirmation remains low. We aimed to determine the diagnostic performance of Xpert-Ultra on stool and urine in presumptive paediatric TB cases in two high-TB burden settings.
METHODS
This Médecins sans Frontières cross-sectional multicentric study took place at Simão Mendes hospital, Guinea-Bissau (July 2019 to April 2020) and in Malakal hospital, South Sudan (April 2021 to June 2023). Children 6 months to 15 years with presumptive TB underwent clinical and laboratory assessment, with one respiratory and/or extrapulmonary sample (gold standard (GS)), one stool and one urine specimen analysed with Xpert-Ultra.
RESULTS
A total of 563 children were enrolled in the study, 133 from Bissau, 400 from Malakal; 30 were excluded. Confirmation of TB was achieved in 75 (14.1%) while 248 (46.5%) had unconfirmed TB. Of 553 with GS specimen, the overall diagnostic yield was 12.4% (66/533). A total of 493 and 524 samples were used to evaluate Xpert-Ultra on stool and on urine, respectively. Compared to GS, sensitivity and specificity of Xpert-Ultra on stool were 62.5%(95%CI:49.4-74) and 98.3%(95%CI:96.7-99.2), whereas on urine were 13.9%(95%CI:7.5-24.3) and 99.4%(95%CI:98.1-99.8), respectively. Nine patients were positive on stool and/or urine but negative on GS.
CONCLUSIONS
Xpert-Ultra on stool showed moderate to high sensitivity and high specificity when compared to GS and an added diagnostic yield when GS was negative. Xpert-Ultra on stool was useful in extrapulmonary cases. Xpert-Ultra in urine showed low test performance.
Conference Material > Video (panel)
Singh SN, Bassat Q, Sondo P, Di Stefano L, Sangma M, et al.
MSF Paediatric Days 2022. 2022 November 30
English
Français
Conference Material > Slide Presentation
Fidelle L, Mahajan R, Gallo J, Biague E, Goncalves R, et al.
MSF Paediatric Days 2024. 2024 May 3; DOI:10.57740/rZE9YDiu3