Screening for HIV Among Patients at Tuberculosis Clinics - Results from Population-Based HIV Impact Assessment Surveys, Malawi, Zambia, and Zimbabwe, 2015-2016.

The World Health Organization and national guidelines recommend HIV testing and counseling at tuberculosis (TB) clinics for all patients, regardless of TB diagnosis (1). Population-based HIV Impact Assessment (PHIA) survey data for 2015-2016 in Malawi, Zambia, and Zimbabwe were analyzed to assess HIV screening at TB clinics among persons who had positive HIV test results in the survey. The analysis was stratified by history of TB diagnosis* (presumptive versus confirmed†), awareness§ of HIV-positive status, antiretroviral therapy (ART)¶ status, and viral load suppression among HIV-positive adults, by history of TB clinic visit. The percentage of adults who reported having ever visited a TB clinic ranged from 4.7% to 9.7%. Among all TB clinic attendees, the percentage who reported that they had received HIV testing during a TB clinic visit ranged from 48.0% to 62.1% across the three countries. Among adults who received a positive HIV test result during PHIA and who did not receive a test for HIV at a previous TB clinic visit, 29.4% (Malawi), 21.9% (Zambia), and 16.2% (Zimbabwe) reported that they did not know their HIV status at the time of the TB clinic visit. These findings represent missed opportunities for HIV screening and linkage to HIV care. In all three countries, viral load suppression rates were significantly higher among those who reported ever visiting a TB clinic than among those who had not (p<0.001). National programs could strengthen HIV screening at TB clinics and leverage them as entry points into the HIV diagnosis and treatment cascade (i.e., testing, initiation of treatment, and viral load suppression).

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA.

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (⍴ ~0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Disease progression and mortality with untreated HIV infection: evidence synthesis of HIV seroconverter cohorts, antiretroviral treatment clinical cohorts and population-based survey data.

INTRODUCTION: Model-based estimates of key HIV indicators depend on past epidemic trends that are derived based on assumptions about HIV disease progression and mortality in the absence of antiretroviral treatment (ART). Population-based HIV Impact Assessment (PHIA) household surveys conducted between 2015 and 2018 found substantial numbers of respondents living with untreated HIV infection. CD4 cell counts measured in these individuals provide novel information to estimate HIV disease progression and mortality rates off ART. METHODS: We used Bayesian multi-parameter evidence synthesis to combine data on (1) cross-sectional CD4 cell counts among untreated adults living with HIV from 10 PHIA surveys, (2) survival after HIV seroconversion in East African seroconverter cohorts, (3) post-seroconversion CD4 counts and (4) mortality rates by CD4 count predominantly from European, North American and Australian seroconverter cohorts. We used incremental mixture importance sampling to estimate HIV natural history and ART uptake parameters used in the Spectrum software. We validated modelled trends in CD4 count at ART initiation against ART initiator cohorts in sub-Saharan Africa. RESULTS: Median untreated HIV survival decreased with increasing age at seroconversion, from 12.5 years [95% credible interval (CrI): 12.1-12.7] at ages 15-24 to 7.2 years (95% CrI: 7.1-7.7) at ages 45-54. Older age was associated with lower initial CD4 counts, faster CD4 count decline and higher HIV-related mortality rates. Our estimates suggested a weaker association between ART uptake and HIV-related mortality rates than previously assumed in Spectrum. Modelled CD4 counts in untreated people living with HIV matched recent household survey data well, though some intercountry variation in frequencies of CD4 counts above 500 cells/mm3 was not explained. Trends in CD4 counts at ART initiation were comparable to data from ART initiator cohorts. An alternate model that stratified progression and mortality rates by sex did not improve model fit appreciably. CONCLUSIONS: Synthesis of multiple data sources results in similar overall survival as previous Spectrum parameter assumptions but implies more rapid progression and longer survival in lower CD4 categories. New natural history parameter values improve consistency of model estimates with recent cross-sectional CD4 data and trends in CD4 counts at ART initiation.