Journal Article > ResearchAbstract Only
AIDS Res Hum Retroviruses. 2016 November 8; Volume 33 (Issue 5); DOI:10.1089/AID.2016.0123
Blaizot S, Kim AA, Zeh C, Riche B, Maman D, et al.
AIDS Res Hum Retroviruses. 2016 November 8; Volume 33 (Issue 5); DOI:10.1089/AID.2016.0123
OBJECTIVES
Estimating HIV incidence is critical for identifying groups at risk for HIV infection, planning and targeting interventions, and evaluating these interventions over time. The use of reliable estimation methods for HIV incidence is thus of high importance. The aim of this study was to compare methods for estimating HIV incidence in a population-based cross-sectional survey.
DESIGN/METHODS
The incidence estimation methods evaluated included assay-derived methods, a testing history-derived method, and a probability-based method applied to data from the Ndhiwa HIV Impact in Population Survey (NHIPS). Incidence rates by sex and age and cumulative incidence as a function of age were presented.
RESULTS
HIV incidence ranged from 1.38 [95% confidence interval (CI) 0.67-2.09] to 3.30 [95% CI 2.78-3.82] per 100 person-years overall; 0.59 [95% CI 0.00-1.34] to 2.89 [95% CI 0.86-6.45] in men; and 1.62 [95% CI 0.16-6.04] to 4.03 [95% CI 3.30-4.77] per 100 person-years in women. Women had higher incidence rates than men for all methods. Incidence rates were highest among women aged 15-24 and 25-34 years and highest among men aged 25-34 years.
CONCLUSION
Comparison of different methods showed variations in incidence estimates, but they were in agreement to identify most-at-risk groups. The use and comparison of several distinct approaches for estimating incidence are important to provide the best-supported estimate of HIV incidence in the population.
Estimating HIV incidence is critical for identifying groups at risk for HIV infection, planning and targeting interventions, and evaluating these interventions over time. The use of reliable estimation methods for HIV incidence is thus of high importance. The aim of this study was to compare methods for estimating HIV incidence in a population-based cross-sectional survey.
DESIGN/METHODS
The incidence estimation methods evaluated included assay-derived methods, a testing history-derived method, and a probability-based method applied to data from the Ndhiwa HIV Impact in Population Survey (NHIPS). Incidence rates by sex and age and cumulative incidence as a function of age were presented.
RESULTS
HIV incidence ranged from 1.38 [95% confidence interval (CI) 0.67-2.09] to 3.30 [95% CI 2.78-3.82] per 100 person-years overall; 0.59 [95% CI 0.00-1.34] to 2.89 [95% CI 0.86-6.45] in men; and 1.62 [95% CI 0.16-6.04] to 4.03 [95% CI 3.30-4.77] per 100 person-years in women. Women had higher incidence rates than men for all methods. Incidence rates were highest among women aged 15-24 and 25-34 years and highest among men aged 25-34 years.
CONCLUSION
Comparison of different methods showed variations in incidence estimates, but they were in agreement to identify most-at-risk groups. The use and comparison of several distinct approaches for estimating incidence are important to provide the best-supported estimate of HIV incidence in the population.
Journal Article > ResearchFull Text
AIDS Res Hum Retroviruses. 2023 April 11; Volume 10 (Issue 1); 1-6.; DOI:10.24966/CMPH-1978/1000125
Temessadouno FW, Hiffler L, Gallo J, Gignoux EM, Domenichini C, et al.
AIDS Res Hum Retroviruses. 2023 April 11; Volume 10 (Issue 1); 1-6.; DOI:10.24966/CMPH-1978/1000125
CONTEXT
The Paediatric Early Warning System (PEWS) is a clinical monitoring tool used routinely in emergency and observation rooms to detect rapid deterioration in paediatric patients, allowing timely action. MSF has been using an adapted version of PEWS in all paediatric projects since 2013 and started using it in the Simao Mendes National Hospital (HNSM) in 2017. The PEWS has not been previously considered as a predictive tool for mortality risk. In this study, we evaluate whether the PEWS could be validated as a paediatric mortality risk score in our Paediatric Intensive Care Unit (PICU) setting.
METHODS
This is an observational study with prospective data collection among children admitted to the HNSM PICU, assessing an adapted version of PEWS on admission, 24 hours after admission, and notification of the outcome of the hospitalization. Data analysis, using State 15.0, was conducted in three stages: description of participants, univariate analysis, and multivariate analysis.
RESULTS
The main analysis showed that the greater the PEWS score, the higher the risk of death. However, only a PEWS score >7 was significantly associated with an increased risk of death, OR =5.9; 95% CI: 2.3 - 12.9, p < 0.001. In addition, having an underlying pathology increased the risk of death, OR=4.2; 95% CI: 1.3 - 13.2, p=0.015. Age was not significantly associated with increased risk of death, which may be due to the small sample size of patients less than one year old. A PEWS score greater than five, 24 hours after admission, indicated a significantly higher risk of death, OR=6.2; 95% CI: 2.8 - 13.6, p < 0.001.
CONCLUSION
Our evaluation of PEWS among children on admission to the PICU found that it could be a simple and useful predictive tool of mortality risk in low resource settings. It may allow better organization of the human resources, and improve the analysis of the mortality ratio, in a PICU. However, adequate follow-up and management of those classified as orange, yellow, or even green by the PEWS should be maintained as the PEWS would fail to identify a significant proportion of patients at risk of death.
The Paediatric Early Warning System (PEWS) is a clinical monitoring tool used routinely in emergency and observation rooms to detect rapid deterioration in paediatric patients, allowing timely action. MSF has been using an adapted version of PEWS in all paediatric projects since 2013 and started using it in the Simao Mendes National Hospital (HNSM) in 2017. The PEWS has not been previously considered as a predictive tool for mortality risk. In this study, we evaluate whether the PEWS could be validated as a paediatric mortality risk score in our Paediatric Intensive Care Unit (PICU) setting.
METHODS
This is an observational study with prospective data collection among children admitted to the HNSM PICU, assessing an adapted version of PEWS on admission, 24 hours after admission, and notification of the outcome of the hospitalization. Data analysis, using State 15.0, was conducted in three stages: description of participants, univariate analysis, and multivariate analysis.
RESULTS
The main analysis showed that the greater the PEWS score, the higher the risk of death. However, only a PEWS score >7 was significantly associated with an increased risk of death, OR =5.9; 95% CI: 2.3 - 12.9, p < 0.001. In addition, having an underlying pathology increased the risk of death, OR=4.2; 95% CI: 1.3 - 13.2, p=0.015. Age was not significantly associated with increased risk of death, which may be due to the small sample size of patients less than one year old. A PEWS score greater than five, 24 hours after admission, indicated a significantly higher risk of death, OR=6.2; 95% CI: 2.8 - 13.6, p < 0.001.
CONCLUSION
Our evaluation of PEWS among children on admission to the PICU found that it could be a simple and useful predictive tool of mortality risk in low resource settings. It may allow better organization of the human resources, and improve the analysis of the mortality ratio, in a PICU. However, adequate follow-up and management of those classified as orange, yellow, or even green by the PEWS should be maintained as the PEWS would fail to identify a significant proportion of patients at risk of death.