Public use dataset from the Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA)'s evaluations of HIV recency assays. Samples tested by CEPHIA were obtained from numerous collaborators. See the acknowledgements below: Funding CEPHIA was supported by grants from the Bill and Melinda Gates Foundation (OPP1017716 to G.M., OPP1062806 to C.D.P. and OPP1115799). Additional support for analysis was provided by a grant from the US National Institutes of Health (R34 MH096606 to C.D.P.) and by the South African Department of Science and Technology and the National Research Foundation. Specimen and data collection were funded in part by grants from the NIH (P01 AI071713, R01 HD074511, P30 AI027763, R24 AI067039, U01 AI043638, P01 AI074621 and R24 AI106039); the HIV Prevention Trials Network (HPTN) sponsored by the NIAID, National Institutes of Child Health and Human Development (NICH/HD), National Institute on Drug Abuse, National Institute of Mental Health, and Office of AIDS Research, of the NIH, DHHS (UM1 AI068613 and R01 AI095068); the California HIV-1 Research Program (RN07-SD-702); Brazilian Program for STD and AIDS, Ministry of Health (914/BRA/3014-UNESCO); and the São Paulo City Health Department (2004-0.168.922– 7). Selected samples from International AIDS Vaccine Initiative (IAVI)-supported cohorts were funded by IAVI with the generous support of USAID and other donors; a full list of IAVI donors is available at www.iavi.org. Acknowledgements The Consortium for the Evaluation and Performance of HIV Incidence Assays (CEPHIA) comprises: Alex Welte, Joseph Sempa, formerly: David Matten, Hilmarie ́ Brand, Trust Chiba- wara (South African Centre for Epidemiological Modelling and Analysis, Stellenbosch Univer- sity); Gary Murphy, Jake Hall, formerly: Elaine Mckinney (Public Health England); Michael P. Busch, Eduard Grebe, Shelley Facente, Dylan Hampton, Sheila Keating, formerly: Mila Lebe- deva (Vitalant Research Institute, formerly Blood Systems Research Institute); Christopher D. Pilcher, Kara Marson (University of California San Francisco); Reshma Kassanjee (University of Cape Town); Oliver Laeyendecker, Thomas Quinn, David Burns (National Institutes of Health); Susan Little (University of California San Diego); Anita Sands (World Health Organi- zation); Tim Hallett (Imperial College London); Sherry Michele Owen, Bharat Parekh, Connie Sexton (Centers for Disease Control and Prevention); Matthew Price, Anatoli Kamali (Interna- tional AIDS Vaccine Initiative); Lisa Loeb (The Options Study—University of California San Francisco); Jeffrey Martin, Steven G Deeks, Rebecca Hoh (The SCOPE Study—University of California San Francisco); Zelinda Bartolomei, Natalia Cerqueira (The AMPLIAR Cohort— University of São Paulo); Breno Santos, Kellin Zabtoski, Rita de Cassia Alves Lira (The AMPLIAR Cohort—Grupo Hospital Conceic ̧ão); Rosa Dea Sperhacke, Leonardo R Motta, Machline Paganella (The AMPLIAR Cohort—Universidade Caxias Do Sul); Esper Kallas, Helena Tomiyama, Claudia Tomiyama, Priscilla Costa, Maria A Nunes, Gisele Reis, Mariana M Sauer, Natalia Cerqueira, Zelinda Nakagawa, Lilian Ferrari, Ana P Amaral, Karine Milani (The São Paulo Cohort—University of São Paulo, Brazil); Salim S Abdool Karim, Quarraisha Abdool Karim, Thumbi Ndungu, Nelisile Majola, Natasha Samsunder (CAPRISA, University of Kwazulu-Natal); Denise Naniche (The GAMA Study—Barcelona Centre for International Health Research); Ina ́cio Mandomando, Eusebio V Macete (The GAMA Study—Fundacao Manhica); Jorge Sanchez, Javier Lama (SABES Cohort—Asociacio ́n Civil Impacta Salud y Educacio ́n (IMPACTA)); Ann Duerr (The Fred Hutchinson Cancer Research Center); Maria R Capobianchi (National Institute for Infectious Diseases “L. Spallanzani”, Rome); Barbara Suligoi (Istituto Superiore di Sanità, Rome); Susan Stramer (American Red Cross); Phillip Wil- liamson (Creative Testing Solutions / Vitalant Research Institute); Marion Vermeulen (South African National Blood Service); and Ester Sabino (Hemocentro do São Paolo).
Background In South Africa, female sex workers (FSWs) are perceived to play a pivotal role in the country's HIV epidemic. Understanding their health status and risk factors for adverse health outcomes is foundational for developing evidence-based health care for this population.Objective Describe the methodology used to successfully implement a community-led study of social and employment circumstances, HIV and associated factors amongst FSWs in South Africa.Method A community-centric, cross-sectional, survey of 3,005 adult FSWs was conducted (January–July 2019) on 12 Sex Work (SW) programme sites across nine provinces of South Africa. Sites had existing SW networks and support programmes providing peer education and HIV services. FSWs were involved in the study design, questionnaire development, and data collection. Questions included: demographic, sexual behaviour, HIV testing and treatment/PrEP history, and violence exposure. HIV rapid testing, viral load, CD4 count, HIV recency, and HIV drug resistance genotypic testing were undertaken. Partner organisations provided follow-up services.Results HIV Prevalence was 61.96%, the median length of selling sex was 6 years, and inconsistent condom use was reported by 81.6% of participants, 88.4% reported childhood trauma, 46.2% reported physical or sexual abuse by an intimate partner and 57.4% by a client. More than half of participants had depression and post-traumatic stress disorder (52.7% and 54.1%, respectively).Conclusion This is the first national survey of HIV prevalence amongst FSWs in programmes in South Africa. The data highlight the vulnerability of this population to HIV, violence and mental ill health, suggesting the need for urgent law reform. Based on the unique methodology and the successful implementation alongside study partners, the outcomes will inform tailored interventions. Our rapid rate of enrolment, low rate of screening failure and low proportion of missing data showed the feasibility and importance of community-centric research with marginalised, highly vulnerable populations.
Background: Diabetes is a chronic disease with severe late complications. It is known to impact the quality of life and cause disability, which may affect an individual's capacity to manage and maintain longer-term health and well-being.Objectives: To examine the prevalence of self-report diabetes, and association between diabetes and each of health-related quality of life and disability amongst South Africa's older adults. To study both the direct relationship between diabetes and these two measures, as well as moderation effects, i.e. whether associations between other factors and these measures of well-being differed between individuals with diabetes and those without.Methods: Secondary analyses of data on participants aged 50 years and older from the Study on global AGEing and adult health (SAGE) in South Africa Wave 1 (2007–2008) were conducted. Prevalence of self-reported diabetes was assessed. Multivariable regressions describe the relationships between each of quality of life (WHOQoL) and disability (WHODAS), and diabetes, while controlling for selected socio-demographic characteristics, health risk behaviours and co-morbid conditions. In the regression models, we also investigated whether diabetes moderates the relationships between these additional factors and WHOQoL/WHODAS.Results: Self-reported diabetes prevalence was 9.2% (95% CI: 7.8,10.9) and increased with age. Having diabetes was associated with poorer WHOQoL scores (additive effect: −4.2; 95% CI: −9.2,0.9; p-value <0.001) and greater disability (multiplicative effect: 2.1; 95% CI: 1.5,2.9; p-value <0.001). Lower quality of life and greater disability were both related to not being in a relationship, lower education, less wealth, lower physical activity and a larger number of chronic conditions.Conclusions: Diabetes is associated with lower quality of life and greater disability amongst older South Africans. Attention needs to be given to enhancing the capacity of health systems to meet the changing needs of ageing populations with diabetes in SA as well as facilitating social support networks in communities.
Temporal trends in cystic fibrosis (CF) survival from low-middle-income settings is poorly reported. We describe changes in CF survival after diagnosis over 40 years from a South African (SA) CF center.An observational cohort study of people diagnosed with CF from 1974 to 2019. Changes in age-specific mortality rates from 2000 (vs. before 2000) were estimated using multivariable Poisson regression. Data were stratified by current age < or ≥10 years and models controlled for diagnosis age, sex, ethnicity, genotype, and Pseudomonas aeruginosa (PA) infection. A second analysis explored the association of mortality with weight and forced expiratory volume in 1 s reported as z-scores (FEV1z-scores) at age 5-8 years.A total of 288 people (52% male; 57% Caucasian; 44% p.Phe508del homozygous) were included (median diagnosis age 0.5 years: Q1, Q3: 0.2, 2.5); 100 (35%) died and 30 (10%) lost to follow-up. Among age >10 years, age-specific mortality from 2000 was significantly lower (adjusted hazard ratio [aHR]: 0.14; 95% confidence interval [CI]: 0.06, 0.29; p < 0.001), but not among age <10 years (aHR: 0.67; 95% CI: 0.28, 1.64; p = 0.383). In children <10 years, Caucasian ethnicity was associated with lower mortality (aHR 0.17; 95% CI: 0.05, 0.63), and longer times since first PA infection with higher mortality (aHR: 1.31; 95% CI: 1.01, 1.68). Mortality was sevenfold higher if FEV1z was <-2.0 at age 5-8 years (aHR: 7.64; 95% CI: 2.58, 22.59).Overall, CF survival has significantly improved in SA from 2000 in people older than 10 years. However, increased risk of mortality persists in young non-Caucasian children, and with FEV1z <-2.0 at age 5-8 years.
Monitoring mother-infant pairs with HIV exposure is needed to assess the effectiveness of vertical transmission (VT) prevention programmes and progress towards VT elimination.
Background: The extent of error, from collection to processing, when measuring PO2, PCO2 and pH in arterial blood samples drawn from critically ill patients with sepsis and leucocytosis, is unknown. Methods: Twenty-nine patients with sepsis and a leucocyte count > 12 000/mm3, who had routine arterial blood analysis were included in the study. Blood was drawn into two 1 ml heparinised glass syringes. One syringe was cooled on ice and tested at 60 minutes. The other syringe was used for analysis at 0, 10, 30 and 60 minutes. Differences in measurements, from the Time-0 results, were described. For PO2, linear mixed models estimated the impact of time to processing, controlling for the potentially confounding and moderating effects of Time-0 leucocyte count and fractional inspired oxygen concentration respectively. Results: PO2 exhibited the most pronounced changes over time at ambient temperature: The mean (SD) relative differences at 10, 30 and 60 minutes were -4.72 (8.82), -13.66 (10.25), and -25.12 (15.55)% respectively; and mean (SD) absolute differences -0.88 (1.49), -2.37 (1.89) and -4.32 (3.06) kPa. For pH, at 60 minutes, the mean (SD) relative and absolute differences were -0.27 (0.45)% and -0.02 (0.03) respectively; for PCO2, 6.16 (7.80)% and 0.25 (0.35) kPa. The median differences for the on-ice 60-minute sample for pH and PCO2 were 0.019 and -0.12 (both P < 0.001), and for PO2 0.100 (P: 0.216). The model estimated that average PO2 decreased by 5% per 10 minute delay in processing (95% CI for effect: 0.94 to 0.96; P < 0.001) at the average leucocyte count, with more rapid declines at higher counts, though with substantial inter-patient variation. Conclusion: Delayed blood gas analysis in samples stored at ambient temperature results in a statistically and clinically significant progressive decrease in arterial PO2, which may alter clinical decision-making in septic patients.
The objective was to compare COVID-19 outcomes in the Omicron-driven fourth wave with prior waves in the Western Cape, assess the contribution of undiagnosed prior infection to differences in outcomes in a context of high seroprevalence due to prior infection and determine whether protection against severe disease conferred by prior infection and/or vaccination was maintained.
Background The development of strategies to better detect and manage patients with multiple long-term conditions requires estimates of the most prevalent condition combinations. However, standard meta-analysis tools are not well suited to synthesising heterogeneous multimorbidity data. Methods We developed a statistical model to synthesise data on associations between diseases and nationally representative prevalence estimates and applied the model to South Africa. Published and unpublished data were reviewed, and meta-regression analysis was conducted to assess pairwise associations between 10 conditions: arthritis, asthma, chronic obstructive pulmonary disease (COPD), depression, diabetes, HIV, hypertension, ischaemic heart disease (IHD), stroke and tuberculosis. The national prevalence of each condition in individuals aged 15 and older was then independently estimated, and these estimates were integrated with the ORs from the meta-regressions in a statistical model, to estimate the national prevalence of each condition combination. Results The strongest disease associations in South Africa are between COPD and asthma (OR 14.6, 95% CI 10.3 to 19.9), COPD and IHD (OR 9.2, 95% CI 8.3 to 10.2) and IHD and stroke (OR 7.2, 95% CI 5.9 to 8.4). The most prevalent condition combinations in individuals aged 15+ are hypertension and arthritis (7.6%, 95% CI 5.8% to 9.5%), hypertension and diabetes (7.5%, 95% CI 6.4% to 8.6%) and hypertension and HIV (4.8%, 95% CI 3.3% to 6.6%). The average numbers of comorbidities are greatest in the case of COPD (2.3, 95% CI 2.1 to 2.6), stroke (2.1, 95% CI 1.8 to 2.4) and IHD (1.9, 95% CI 1.6 to 2.2). Conclusion South Africa has high levels of HIV, hypertension, diabetes and arthritis, by international standards, and these are reflected in the most prevalent condition combinations. However, less prevalent conditions such as COPD, stroke and IHD contribute disproportionately to the multimorbidity burden, with high rates of comorbidity. This modelling approach can be used in other settings to characterise the most important disease combinations and levels of comorbidity.
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