100 results found
Hsieh YL, Jahn A, Menzies NA, et al., 2020, An evaluation of 6-month versus continuous isoniazid preventive therapy for M. tuberculosis in adults living with HIV/AIDS in Malawi., J Acquir Immune Defic Syndr
BACKGROUND: To assist the Malawi Ministry of Health to evaluate two competing strategies for scale-up of isoniazid preventive therapy (IPT) among HIV-positive adults receiving ART. SETTING: Malawi. METHODS: We used a multi-district, compartmental model of the Malawi TB/HIV epidemic to compare the anticipated health impacts of 6-month versus continuous IPT programs over a 12-year horizon, while respecting a US$10.8 million constraint on drug costs in the first three years. RESULTS: The 6-month IPT program could be implemented nationwide while the continuous IPT alternative could be introduced in 14 (out of 27) districts. By the end of year 12, the continuous IPT strategy was predicted to avert more TB cases than the 6-month alternative, although not statistically significantly (2368 additional cases averted; 95%PI, -1459, 5023). The 6-month strategy required fewer person-years of IPT to avert a case of TB or death than the continuous strategy. For both programs, the mean reductions in TB incidence among PLHIV by year 12 were expected to be <10%, and the cumulative numbers of IPT-related hepatotoxicity to exceed the number of all-cause deaths averted in the first three years. CONCLUSION: With the given budgetary constraint, nationwide implementation of 6-month IPT would be more efficient and yield comparable health benefits than implementing continuous IPT program in fewer districts. The anticipated health effects associated with both IPT strategies suggested a combination of different TB intervention strategies would likely be required to yield greater impact on TB control in settings like Malawi, where ART coverage is relatively high.
Hogan A, Jewell B, Sherrard-Smith E, et al., 2020, Potential impact of the COVID-19 pandemic on HIV, TB and malaria in low- and middle-income countries: a modelling study, The Lancet Global Health, Vol: 8, Pages: e1132-e1141, ISSN: 2214-109X
Background: COVID-19 has the potential to cause substantial disruptions to health services, including by cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions in services for human immunodeficiency virus (HIV), tuberculosis (TB) and malaria in low- and middle-income countries with high burdens of those disease could lead to additional loss of life. Methods: We constructed plausible scenarios for the disruptions that could be incurred during the COVID-19 pandemic and used established transmission models for each disease to estimate the additional impact on health that could be caused in selected settings.Findings: In high burden settings, HIV-, TB- and malaria-related deaths over five years may increase by up to 10%, 20% and 36%, respectively, compared to if there were no COVID-19 pandemic. We estimate the greatest impact on HIV to be from interruption to antiretroviral therapy, which may occur during a period of high health system demand. For TB, we estimate the greatest impact is from reductions in timely diagnosis and treatment of new cases, which may result from any prolonged period of COVID-19 suppression interventions. We estimate that the greatest impact on malaria burden could come from interruption of planned net campaigns. These disruptions could lead to loss of life-years over five years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV/TB epidemics.Interpretation: Maintaining the most critical prevention activities and healthcare services for HIV, TB and malaria could significantly reduce the overall impact of the COVID-19 pandemic.Funding: Bill & Melinda Gates Foundation, The Wellcome Trust, DFID, MRC
Flaxman S, Mishra S, Gandy A, et al., 2020, Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe, Nature, Vol: 584, Pages: 257-261, ISSN: 0028-0836
Following the emergence of a novel coronavirus1 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions such as closure of schools and national lockdowns. We study the impact of major interventions across 11 European countries for the period from the start of COVID-19 until the 4th of May 2020 when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. We use partial pooling of information between countries with both individual and shared effects on the reproduction number. Pooling allows more information to be used, helps overcome data idiosyncrasies, and enables more timely estimates. Our model relies on fixed estimates of some epidemiological parameters such as the infection fatality rate, does not include importation or subnational variation and assumes that changes in the reproduction number are an immediate response to interventions rather than gradual changes in behavior. Amidst the ongoing pandemic, we rely on death data that is incomplete, with systematic biases in reporting, and subject to future consolidation. We estimate that, for all the countries we consider, current interventions have been sufficient to drive the reproduction number Rt below 1 (probability Rt< 1.0 is 99.9%) and achieve epidemic control. We estimate that, across all 11 countries, between 12 and 15 million individuals have been infected with SARS-CoV-2 up to 4th May, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions and lockdown in particular have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
Unwin H, Mishra S, Bradley VC, et al., 2020, Report 23: State-level tracking of COVID-19 in the United States
our estimates show that the percentage of individuals that have been infected is 4.1% [3.7%-4.5%], with widevariation between states. For all states, even for the worst affected states, we estimate that less than a quarter of thepopulation has been infected; in New York, for example, we estimate that 16.6% [12.8%-21.6%] of individuals have beeninfected to date. Our attack rates for New York are in line with those from recent serological studies  broadly supportingour choice of infection fatality rate.There is variation in the initial reproduction number, which is likely due to a range of factors; we find a strong associationbetween the initial reproduction number with both population density (measured at the state level) and the chronologicaldate when 10 cumulative deaths occurred (a crude estimate of the date of locally sustained transmission).Our estimates suggest that the epidemic is not under control in much of the US: as of 17 May 2020 the reproductionnumber is above the critical threshold (1.0) in 24 [95% CI: 20-30] states. Higher reproduction numbers are geographicallyclustered in the South and Midwest, where epidemics are still developing, while we estimate lower reproduction numbersin states that have already suffered high COVID-19 mortality (such as the Northeast). These estimates suggest that cautionmust be taken in loosening current restrictions if effective additional measures are not put in place.We predict that increased mobility following relaxation of social distancing will lead to resurgence of transmission, keepingall else constant. We predict that deaths over the next two-month period could exceed current cumulative deathsby greater than two-fold, if the relationship between mobility and transmission remains unchanged. Our results suggestthat factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offsetthe rise of transmission associated with loosening of social distancing. Overall, we
Mellan T, Hoeltgebaum H, Mishra S, et al., 2020, Report 21: Estimating COVID-19 cases and reproduction number in Brazil
Brazil is an epicentre for COVID-19 in Latin America. In this report we describe the Brazilian epidemicusing three epidemiological measures: the number of infections, the number of deaths and the reproduction number. Our modelling framework requires sufficient death data to estimate trends, and wetherefore limit our analysis to 16 states that have experienced a total of more than fifty deaths. Thedistribution of deaths among states is highly heterogeneous, with 5 states—São Paulo, Rio de Janeiro,Ceará, Pernambuco and Amazonas—accounting for 81% of deaths reported to date. In these states, weestimate that the percentage of people that have been infected with SARS-CoV-2 ranges from 3.3% (95%CI: 2.8%-3.7%) in São Paulo to 10.6% (95% CI: 8.8%-12.1%) in Amazonas. The reproduction number (ameasure of transmission intensity) at the start of the epidemic meant that an infected individual wouldinfect three or four others on average. Following non-pharmaceutical interventions such as school closures and decreases in population mobility, we show that the reproduction number has dropped substantially in each state. However, for all 16 states we study, we estimate with high confidence that thereproduction number remains above 1. A reproduction number above 1 means that the epidemic isnot yet controlled and will continue to grow. These trends are in stark contrast to other major COVID19 epidemics in Europe and Asia where enforced lockdowns have successfully driven the reproductionnumber below 1. While the Brazilian epidemic is still relatively nascent on a national scale, our resultssuggest that further action is needed to limit spread and prevent health system overload.
Vollmer M, Mishra S, Unwin H, et al., 2020, Report 20: A sub-national analysis of the rate of transmission of Covid-19 in Italy
Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28; 238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a
Hogan A, Jewell B, Sherrard-Smith E, et al., 2020, Report 19: The potential impact of the COVID-19 epidemic on HIV, TB and malaria in low- and middle-income countries
COVID-19 has the potential to cause disruptions to health services in different ways; through the health system becoming overwhelmed with COVID-19 patients, through the intervention used to slow transmission of COVID-19 inhibiting access to preventative interventions and services, and through supplies of medicine being interrupted. We aim to quantify the extent to which such disruptions in services for HIV, TB and malaria in high burden low- and middle-income countries could lead to additional loss of life. In high burden settings, HIV, TB and malaria related deaths over 5 years may be increased by up to 10%, 20% and 36%, respectively, compared to if there were no COVID-19 epidemic. We estimate the greatest impact on HIV to be from interruption to ART, which may occur during a period of high or extremely high health system demand; for TB, we estimate the greatest impact is from reductions in timely diagnosis and treatment of new cases, which may result from a long period of COVID-19 suppression interventions; for malaria, we estimate that the greatest impact could come from reduced prevention activities including interruption of planned net campaigns, through all phases of the COVID-19 epidemic. In high burden settings, the impact of each type of disruption could be significant and lead to a loss of life-years over five years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV/TB epidemics. Maintaining the most critical prevention activities and healthcare services for HIV, TB and malaria could significantly reduce the overall impact of the COVID-19 epidemic.
Flaxman S, Mishra S, Gandy A, et al., 2020, Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproducti
Atchison C, Bowman L, Eaton J, et al., 2020, Report 10: Public response to UK Government recommendations on COVID-19: population survey, 17-18 March 2020, 10
On Monday 16th March 2020 the UK government announced new actions to control COVID-19. These recommendations directly affected the entire UK population, and included the following: stop non-essential contact with others; stop all unnecessary travel; start working from home where possible; avoid pubs, clubs, theatres and other such social venues; and to isolate at home for 14 days if anyone in the household has a high temperature or a new and continuous cough. To capture public sentiment towards these recommendations, a YouGov survey was commissioned by the Patient Experience Research Centre (PERC), Imperial College London. The survey was completed by 2,108 UK adults between the dates of 17th – 18th March 2020. The survey results show the following:• 77% reported being worried about the COVID-19 outbreak in the UK.• 48% of adults who have not tested positive for COVID-19 believe it is likely they will be infected at some point in the future.• 93% of adults reported personally taking at least one measure to protect themselves from COVID-19 infection, including:o 83% washed their hands more frequently;o 52% avoided crowded areas;o 50% avoided social events;o 36% avoided public transport;o 31% avoided going out;o 11% avoided going to work;o 28% avoided travel to areas outside the UK.• There is high reported ability and willingness to self-isolate for 7 days* if advised to do so by a health professional (88%).• However only 44% reported being able to work from home. This was higher among managerial and professional workers (60%) than manual, semi-skilled, and casual workers (19%)^, reflecting less flexible job roles for manual and lower grade workers. • 71% reported changing behaviour in response to government guidance. The figure (53%) was lower for young adults (18-24 year-olds).• Hand washing (63%), avoiding persons with symptoms (61%), and covering your sneeze (53%) were more likely to be perceived as ‘very effective&rs
Sheng B, Eaton JW, Mahy M, et al., 2020, Comparison of HIV prevalence among antenatal clinic attendees estimated from routine testing and unlinked anonymous testing, Statistics in Biosciences, Pages: 1-16, ISSN: 1867-1772
In 2015, WHO and UNAIDS released new guidance recommending that countries transition from conducting antenatal clinic (ANC) unlinked anonymous testing (ANC-UAT) for tracking HIV prevalence trends among pregnant women to using ANC routine testing (ANC-RT) data, which are more consistent and economic to collect. This transition could pose challenges for distinguishing whether changes in observed prevalence are due to a change in underlying population prevalence or due to a change in the testing approach. We compared the HIV prevalence measured from ANC-UAT and ANC-RT in 15 countries that had both data sources in overlapping years. We used linear mixed-effects model (LMM) to estimate the RT-to-UAT calibration parameter as well as other unobserved quantities. We summarized the results at different levels of aggregation (e.g., country, urban, rural, and province). Based on our analysis, the HIV prevalence measured by ANC-UAT and ANC-RT data are consistent in most countries. Therefore, if large discrepancy is observed between ANC-UAT and ANC-RT at the same location, we recommend that people should be cautious and investigate the reason. For countries that lack information to estimate the calibration parameter, we propose an informative prior distribution of mean 0 and standard deviation 0.2 for the RT-to-UAT calibration parameter.
Berman P, Revill P, Phillips A, et al., 2020, Modelling and Economic Evaluation to Inform WHO HIV Treatment Guidelines, World Scientific Series in Global Health Economics and Public Policy, Pages: 275-285
© 2020 The Author(s). International organizations influence national-level health sector priorities by affecting how much funding is available for health care delivery within countries and how that funding is used. The setting of guidelines for the management of diseases (e.g. for malaria, child health, nutrition) by the World Health Organization (WHO) exerts particular influence. Guidelines typically provide syntheses of evidence on clinical efficacy and effectiveness and make recommendations for health care best practice. However, for the most part, they do not well inform the allocation of limited available health care resources. Consequentially, they risk encouraging national and international decision-makers to divert resources away from areas of greater potential gains in population health. In this case study, we reflect upon efforts to incorporate economic evidence into the development of the WHO HIV Treatment Guidelines. We describe how the WHO has incorporated economic insight into these and other guidelines. However, even in this case, the processes currently followed for guideline development can limit the extent to which recommendations can draw upon economic evidence. Changes in the way WHO Guidelines are developed and interpreted, and how evidence is used to inform decision-making at the country level, is therefore required. We give our thoughts on what these changes could be.
Eaton JW, Brown T, Puckett R, et al., 2019, The estimation and projection package age-sex model and the r-hybrid model: new tools for estimating HIV incidence trends in sub-Saharan Africa., AIDS, Vol: 33, Pages: S235-S244, ISSN: 0269-9370
OBJECTIVES: Improve models for estimating HIV epidemic trends in sub-Saharan Africa (SSA). DESIGN: Mathematical epidemic model fit to national HIV survey and ANC sentinel surveillance (ANC-SS) data. METHODS: We modified EPP to incorporate age and sex stratification (EPP-ASM) to more accurately capture the shifting demographics of maturing HIV epidemics. Secondly, we developed a new functional form for the HIV transmission rate, termed 'r-hybrid', which combines a four-parameter logistic function for the initial epidemic growth, peak, and decline followed by a first-order random walk for recent trends after epidemic stabilization. We fitted the r-hybrid model along with previously developed r-spline and r-trend models to HIV prevalence data from household surveys and ANC-SS in 177 regions in 34 SSA countries. We used leave-one-out cross validation with household survey HIV prevalence to compare model predictions. RESULTS: The r-hybrid and r-spline models typically provided similar HIV prevalence trends, but sometimes qualitatively different assessments of recent incidence trends because of different structural assumptions about the HIV transmission rate. The r-hybrid model had the lowest average continuous ranked probability score, indicating the best model predictions. Coverage of 95% posterior predictive intervals was 91.5% for the r-hybrid model, versus 87.2 and 85.5% for r-spline and r-trend, respectively. CONCLUSION: The EPP-ASM and r-hybrid models improve consistency of EPP and Spectrum, improve the epidemiological assumptions underpinning recent HIV incidence estimates, and improve estimates and short-term projections of HIV prevalence trends. Countries that use general population survey and ANC-SS data to estimate HIV epidemic trends should consider using these tools.
Maheu-Giroux M, Jahn A, Kalua T, et al., 2019, HIV surveillance based on routine testing data from antenatal clinics in Malawi (2011–2018): measuring and adjusting for bias from imperfect testing coverage, AIDS, Vol: 33, Pages: S295-S302, ISSN: 0269-9370
Objective: The use of routinely collected data from prevention of mother-to-child transmission programs (ANC-RT) has been proposed to monitor HIV epidemic trends. This poses several challenges for surveillance, one of them being that women may opt-out of testing and/or test stock-outs may result in inconsistent service availability. In this study, we sought to empirically quantify the relationship between imperfect HIV testing coverage and HIV prevalence among pregnant women from ANC-RT data.Design: We used reports from the ANC Register of all antenatal care (ANC) sites in Malawi (2011–2018), including 49 244 monthly observations, from 764 facilities, totaling 4 375 777 women.Methods: Binomial logistic regression models with facility-level fixed effects and marginal standardization were used to assess the effect of testing coverage on HIV prevalence.Results: Testing coverage increased from 78 to 98% over 2011–2018. We estimated that, had testing coverage been perfect, prevalence would have been 0.4% point lower (95% CI 0.3–0.5%) than the 7.9% observed prevalence, a relative overestimation of 6%. Bias in HIV prevalence was the highest in 2012, when testing coverage was lowest (72%), resulting in a relative overestimation of HIV prevalence of 15% (95% CI 12–17%). Overall, adjustments for imperfect testing coverage led to a subtler decline in HIV prevalence over 2011--2018.Conclusion: Malawi achieved high coverage of routine HIV testing in recent years. Nevertheless, imperfect testing coverage can lead to overestimation of HIV prevalence among pregnant women when coverage is suboptimal. ANC-RT data should be carefully evaluated for changes in testing coverage and completeness when used to monitor epidemic trends.
Maheu-Giroux M, Marsh K, Doyle C, et al., 2019, National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the "first 90" from program and survey data, AIDS, Vol: 33, Pages: S255-S269, ISSN: 0269-9370
OBJECTIVE: HIV testing services (HTS) are a crucial component of national HIV responses. Learning one's HIV diagnosis is the entry point to accessing life-saving antiretroviral treatment and care. Recognizing the critical role of HTS, the Joint United Nations Programme on HIV/AIDS (UNAIDS) launched the 90-90-90 targets stipulating that by 2020, 90% of people living with HIV know their status, 90% of those who know their status receive antiretroviral therapy, and 90% of those on treatment have a suppressed viral load. Countries will need to regularly monitor progress on these three indicators. Estimating the proportion of people living with HIV who know their status (i.e., the "first 90"), however, is difficult. METHODS: We developed a mathematical model (henceforth referred to as "F90") that formally synthesizes population-based survey and HTS program data to estimate HIV status awareness over time. The proposed model uses country-specific HIV epidemic parameters from the standard UNAIDS Spectrum model to produce outputs that are consistent with other national HIV estimates. The F90 model provides estimates of HIV testing history, diagnosis rates, and knowledge of HIV status by age and sex. We validate the F90 model using both in-sample comparisons and out-of-sample predictions using data from three countries: Côte d'Ivoire, Malawi, and Mozambique. RESULTS: In-sample comparisons suggest that the F90 model can accurately reproduce longitudinal sex-specific trends in HIV testing. Out-of-sample predictions of the fraction of PLHIV ever tested over a 4-to-6-year time horizon are also in good agreement with empirical survey estimates. Importantly, out-of-sample predictions of HIV knowledge are consistent (i.e., within 4% points) with those of the fully calibrated model in the three countries when HTS program data are included. The F90 model's predictions of knowledge of status are higher than available self-reported HIV awareness estimates, howe
Johnson LF, Anderegg N, Zaniewski E, et al., 2019, Global variations in mortality in adults after initiating antiretroviral treatment: an updated analysis of the International epidemiology Databases to Evaluate AIDS cohort collaboration., AIDS, Vol: 33, Pages: S283-S294, ISSN: 0269-9370
BACKGROUND: UNAIDS models use data from the International epidemiology Databases to Evaluate AIDS (IeDEA) collaboration in setting assumptions about mortality rates after antiretroviral treatment (ART) initiation. This study aims to update these assumptions with new data, to quantify the extent of regional variation in ART mortality and to assess trends in ART mortality. METHODS: Adult ART patients from Africa, Asia and the Americas were included if they had a known date of ART initiation during 2001-2017 and a baseline CD4 cell count. In cohorts that relied only on passive follow-up (no patient tracing or linkage to vital registration systems), mortality outcomes were imputed in patients lost to follow-up based on a meta-analysis of tracing study data. Poisson regression models were fitted to the mortality data. RESULTS: 464 048 ART patients were included. In multivariable analysis, mortality rates were lowest in Asia and highest in Africa, with no significant differences between African regions. Adjusted mortality rates varied significantly between programmes within regions. Mortality rates in the first 12 months after ART initiation were significantly higher during 2001-2006 than during 2010-2014, although the difference was more substantial in Asia and the Americas [adjusted incidence rate ratio (aIRR) 1.43, 95% CI: 1.22-1.66] than in Africa (aIRR 1.07, 95% CI: 1.04-1.11). CONCLUSION: There is substantial variation in ART mortality between and within regions, even after controlling for differences in mortality by age, sex, baseline CD4 category and calendar period. ART mortality rates have declined substantially over time, although declines have been slower in Africa.
Mahiane SG, Marsh K, Glaubius R, et al., 2019, Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool., AIDS, Vol: 33, Pages: S245-S253, ISSN: 0269-9370
OBJECTIVE: The United Nations Program on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15-49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for United Nations Program on HIV/AIDS' 2019 estimate round. METHODS: Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the 'r-logistic' model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4 at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. RESULTS: We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4 at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10-0.45] in 1990 to about 0.77 (IQR: 0.50-0.89) in 2017. The mean CD4 at diagnosis appeared to be stable, decreasing from 410 cells/&m
Case K, Johnson L, Mahy M, et al., Summarizing the results and methods of the 2019 Joint United Nations Programme on HIV/AIDS HIV estimates, AIDS, ISSN: 0269-9370
Marsh K, Eaton JW, Mahy M, et al., 2019, Global, regional and country-level 90-90-90 estimates for 2018: assessing progress towards the 2020 target., AIDS, Vol: 33, Pages: S213-S226, ISSN: 0269-9370
BACKGROUND: In 2014, the Joint United Nations Programme on HIV/AIDS (UNAIDS) and partners set the 90-90-90 target for the year 2020: diagnose 90% of all people living with HIV (PLHIV); treat 90% of people who know their status; and suppress the virus in 90% of people on treatment. In 2015, countries began reporting to UNAIDS on progress against 90-90-90 using standard definitions and methods. METHODS: We used data submitted to UNAIDS from 170 countries to assess country-specific progress towards 90-90-90 through 2018. To assess global and regional progress, overall and by sex for adults 15 years and older, we combined country-reported data with estimates generated with a Bayesian hierarchical model. RESULTS: A total of 60 countries reported on all three 90s in 2018, up from 23 in 2015. Among all PLHIV worldwide, 79% (67-92%) knew their HIV status. Of these, 78% (69-82%) were accessing treatment and 86% (72-92%) of people accessing treatment had suppressed viral loads. Of the 37.9 million (32.7-44.0 million) PLHIV worldwide, 53% (43-63%) had suppressed viral loads. The gap to fully achieving 73% of PLHIV with suppressed viral load was 7.7 million; 15 countries had already achieved this target by 2018. CONCLUSION: Increased data availability has led to improved measures of country and global progress towards the 90-90-90 target. Although gains in access to testing and treatment continue, many countries and regions are unlikely to reach 90-90-90 by 2020.
Phillips AN, Cambiano V, Nakagawa F, et al., 2019, Cost-per-diagnosis as a metric for monitoring cost effectiveness of HIV testing programmes in low income settings in southern Africa: health economic and modelling analysis, Journal of the International AIDS Society, Vol: 22, Pages: 1-10, ISSN: 1758-2652
Introduction: As prevalence of undiagnosed HIV declines, it is unclear whether testing programmes will be cost effective. To guide their HIV testing programmes,countries require appropriatemetrics that can be measured. The cost-per-diagnosisis potentially a useful metric. Methods:We simulated a series of setting-scenarios for adult HIV epidemics and ART programmes typical of settings in southern Africa using an individual-based model and projected forward from 2018 under two policies: (i) a minimum package of “core” testing (i.e. testing in pregnant women, for diagnosis of symptoms, in sex workers, and in men coming forward for circumcision) is conducted, and (ii) “core” testing as above plus “additional-testing”, for which we specify different rates of testing and various degrees to which those with HIV are more likely to test than thosewithout HIV. We also considered a plausible range of unit test costs. The aim was to assess the relationship between cost-per-diagnosisand the incremental cost-effectiveness ratio(ICER) of the additional-testingpolicy. Discount rate 3%; costs in 2018 $US. Results:There was a strong graded relationship between the cost-per-diagnosisand the ICER. Overall, the ICERwas below $500 per-DALY-averted (the cost effectiveness threshold used in primary analysis) so long as thecost-per-diagnosiswas below $315. This thresholdcost-per-diagnosiswas similar according to epidemic and programmatic features including the prevalence of undiagnosed HIV, the HIV incidence and a measure of HIV programme quality (the proportion of HIV diagnosed people having a viral load <1000 copies/mL). However, restrictingto women, additional-testingdid not appear cost-effective even at acost-per-diagnosisof below $50, while restrictingto men additional-testingwas cost effective up to a cost-per-diagnosisof $585. Thethreshold cost for testing in men fell to $256 when the cost effectiveness threshold was $300instead of $5
Watson O, FitzJohn R, Eaton J, 2019, rdhs: an R package to interact with The Demographic and Health Surveys (DHS) Program datasets [version 1; peer review: 1 approved, 1 approved with reservations], Wellcome Open Research, Vol: 4, Pages: 1-13, ISSN: 2398-502X
Since 1985, the Demographic and Health Surveys (DHS) Program has conducted more than 400 surveys in over 90 countries. These surveys provide decision markers with key measures of population demographics, health and nutrition, which allow informed policy evaluation to be made. Though standard health indicators are routinely published in survey final reports, much of the value of DHS is derived from the ability to download and analyse standardised microdata datasets for subgroup analysis, pooled multi-country analysis, and extended research studies. We have developed an open-source freely available R package ‘rdhs’ to facilitate management and processing of DHS survey data. The package provides a suite of tools to (1) access standard survey indicators through the DHS Program API, (2) identify all survey datasets that include a particular topic or indicator relevant to a particular analysis, (3) directly download survey datasets from the DHS website, (4) load datasets and data dictionaries into R, and (5) extract variables and pool harmonised datasets for multi-survey analysis. We detail the core functionality of ‘rdhs’ by demonstrating how the package can be used to firstly compare trends in the prevalence of anaemia among women between countries before conducting secondary analysis to assess for the relationship between education and anemia.
Dwyer-Lindgren L, Cork MA, Sligar A, et al., 2019, Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017, Nature, Vol: 570, Pages: 189-193, ISSN: 0028-0836
HIV/AIDS is a leading cause of disease burden in sub-Saharan Africa. Existing evidence has demonstrated that there is substantial local variation in the prevalence of HIV; however, subnational variation has not been investigated at a high spatial resolution across the continent. Here we explore within-country variation at a 5 × 5-km resolution in sub-Saharan Africa by estimating the prevalence of HIV among adults (aged 15-49 years) and the corresponding number of people living with HIV from 2000 to 2017. Our analysis reveals substantial within-country variation in the prevalence of HIV throughout sub-Saharan Africa and local differences in both the direction and rate of change in HIV prevalence between 2000 and 2017, highlighting the degree to which important local differences are masked when examining trends at the country level. These fine-scale estimates of HIV prevalence across space and time provide an important tool for precisely targeting the interventions that are necessary to bringing HIV infections under control in sub-Saharan Africa.
Kufa T, Shubber Z, MacLeod W, et al., 2019, CD4 count recovery and associated factors among individuals enrolled in the South African antiretroviral therapy programme: An analysis of national laboratory based data, PLoS ONE, Vol: 14, ISSN: 1932-6203
BackgroundWe describe CD4 count recovery among HIV positive individuals who initiated antiretroviral therapy (ART) with and without severe immune suppression using complete laboratory data from South Africa’s national HIV treatment programme between 2010 and 2014 and discuss implications for CD4 count monitoring.MethodsRetrospective analysis of routinely collected laboratory data from South Africa’s National Health Laboratory Service (NHLS). A probabilistic record linkage algorithm was used to create a cohort of HIV positive individuals who initiated ART between 2010 and 2014 based on timing of CD4 count and viral load measurements. A CD4 count < 50 copies/μl at ART initiation was considered severe immunosuppression. A multivariable piecewise mixed-effects linear regression model adjusting for age, gender, year of starting ART, viral suppression in follow up and province was used to predict CD4 counts during follow up.Results1,070,900 individuals had evidence of starting ART during 2010–2014 and met the criteria for inclusion in the cohort -46.6% starting ART with CD4 < 200 cells/μl and 10.1% with CD4 < 50 cells/ μl. For individuals with CD4 counts < 200 cells/μl, predicted CD4 counts > 200 cells/μl, >350 cells/μl and >500 cells/μl corresponded with mean follow up durations of 1.5 years (standard deviation [s.d] 1.1), 1.9years (s.d 1.2) and 2.1 years (s.d 1.3 years). For those with CD4 counts < 50 cells/μl, predicted CD4 count above these threshold corresponded with mean follow up durations of 2.5 years (s.d 0.9 years), 4.4 years (s.d 0.4 years) and 5.0 years (s.d 0.1years) for recovery to the same thresholds. CD4 count recovery varied mostly with duration on ART, CD4 count at the start of ART and gender.ConclusionFor individuals starting with ART with severe immunosuppression, CD4 recovery to 200cells/μl did not occur or took longer than 12 month for significant proportions. CD4 monitoring and int
Nabukalu D, Reniers G, Risher KA, et al., 2019, Population-level adult mortality following the expansion of antiretroviral therapy in Rakai, Uganda, Population Studies, Vol: 74, Pages: 93-102, ISSN: 0032-4728
There are limited data on the impact of antiretroviral therapy (ART) on population-level adult mortality in sub-Saharan Africa. We analysed data for 2000–14 from the Rakai Community Cohort Study (RCCS) in Uganda, where free ART was scaled up after 2004. Using non-parametric and parametric (Weibull) survival analysis, we estimated trends in average person-years lived between exact ages 15 and 50, per capita life-years lost to HIV, and the mortality hazards of people living with HIV (PLHIV). Between 2000 and 2014, average adult life-years lived before age 50 increased significantly, from 26.4 to 33.5 years for all women and from 28.6 to 33.8 years for all men. As of 2014, life-years lost to HIV had declined significantly, to 1.3 years among women and 0.4 years among men. Following the roll-out of ART, mortality reductions among PLHIV were initially larger in women than men, but this is no longer the case
Eaton J, Terris-Prestholt F, Cambiano V, et al., 2019, Optimizing HIV testing services in sub-Saharan Africa: Cost and performance of verification testing with HIV self-tests and tests for triage, Journal of the International AIDS Society, Vol: 22, ISSN: 1758-2652
Introduction:Strategies employinga single rapid diagnostic test (RDT) such as HIV self-testing (HIVST)or ‘test for triage’ (T4T)areproposed to increase HIV testing programme impact.Current guidelines recommend serial testing with two or three RDTs for HIV diagnosis, followed by retestingwith the same algorithmto verify HIV-positive statusbefore anti-retroviral therapy (ART) initiation. We investigated whether clientspresenting to HTS following a single reactive RDTmust undergo thediagnostic algorithm twice to diagnose and verify HIV-positive status, or whether a diagnosis with the setting-specific algorithm is adequate for ART initiation.Methods: We calculated (1)expected number of false-positive (FP) misclassifications per 10,000 HIV negative persons tested,(2)positive predictive value (PPV) of the overall HIV testingstrategy compared to WHO recommended PPV ≥99%, and (3) expected cost per FPmisclassified person identified by additional verification testingin a typical low-/middle-income setting, compared to the expected lifetime ART cost of $3000. Scenarios considered were: 10% prevalence using two serial RDTsfor diagnosis,1% prevalence using three serial RDTs,and calibrationusing programmatic data from Malawi in 2017where theproportion of people testing HIV positive in facilities was 4%. Results: In the 10% HIV prevalence settingwith a triage test, the expected number ofFP misclassifications was0.86 per 10,000 tested without verification testing and the PPV was 99.9%. In the 1% prevalence setting, expected FP misclassifications were 0.19 with 99.8% PPV, and in the Malawi 2017 calibrated setting the expected misclassifications were 0.08 with 99.98% PPV. The cost per FP identified by verification testing was $5,879, $3,770, and $24,259, respectively. Results were sensitive to assumptions about accuracy of self-reported reactive results and whether reactive triage test results influenced biased interpretation of subsequent RDT results by the HTS provid
Olney JJ, Eaton JW, Braitstein P, et al., 2019, Response to questionable assumptions mar modelling of Kenya home-based testing campaigns - a comment on "Optimal timing of HIV home-based counselling and testing rounds in Western Kenya" (Olney et al. 2018), Journal of the International AIDS Society, Vol: 22, ISSN: 1758-2652
Eaton J, Grebe E, Welte A, et al., 2018, Prevalence and Incidence Calculator (UNAIDS RG)
Calculates HIV incidence from prevalence survey data that include biomarkers of recent infection.Built using inctools for the UNAIDS Reference Group on Estimates, Modelling and Projections.The tool can be accessed at https://incidence.shinyapps.io/incidence_calculator/.
Rentsch C, Reniers G, Machemba R, et al., 2018, Non-disclosure of HIV testing history in population-based surveys: implications for estimating a UNAIDS 90-90-90 target, Global Health Action, Vol: 11, ISSN: 1654-9880
Background: HIV/AIDS programmes and organisations around the world use routinely updated estimates of the UNAIDS 90-90-90 targets to track progress and prioritise further programme implementation. Any bias in these estimates has the potential to mislead organisations on where gaps exist in HIV testing and treatment programmes.Objective: To measure the extent of undisclosed HIV testing history and its impact on estimating the proportion of people living with HIV (PLHIV) who know their HIV status (the ‘first 90’ of the UNAIDS 90-90-90 targets).Methods: We conducted a retrospective cohort study using population-based HIV serological surveillance conducted between 2010 and 2016 and linked, directly observed HIV testing records in Kisesa, Tanzania. Generalised estimating equations logistic regression models were used to detect associations with non-disclosure of HIV testing history adjusting for demographic, behavioural, and clinical characteristics. We compared estimates of the ‘first 90’ using self-reported survey data only and augmented estimates using information from linked records to quantify the absolute and relative impact of undisclosed HIV testing history.Results: Numbers of participants in each of the survey rounds ranged from 7171 to 7981 with an average HIV prevalence of 6.9%. Up to 33% of those who tested HIV-positive and 34% of those who tested HIV-negative did not disclose their HIV testing history. The proportion of PLHIV who reported knowing their status increased from 34% in 2010 to 65% in 2016. Augmented estimates including information from directly observed testing history resulted in an absolute impact of 6.7 percentage points and relative impact of 12.4%.Conclusions: In this population, self-reported testing history in population-based HIV serological surveys under-estimated the percentage of HIV positives that are diagnosed by a relative factor of 12%. Research should be employed in other surveillance systems that benefit f
Woods B, Rothery C, Anderson S-J, et al., 2018, Appraising the value of evidence generation activities: An HIV Modelling Study, BMJ Global Health, Vol: 3, ISSN: 2059-7908
Introduction: The generation of robust evidence has been emphasised as a priority for global health. Evidence generation spans a wide range of activities including clinical trials, surveillance programmes and health system performance measurement. As resources for healthcare and research are limited, the desirability of research expenditure should be assessed on the same basis as other healthcare resources, that is, the health gains from research must be expected to exceed the health opportunity costs imposed as funds are diverted to research rather than service provision.Methods: We developed a transmission and costing model to examine the impact of generating additional evidence to reduce uncertainties on the evolution of a generalised HIV epidemic in Zambia.Results: We demonstrate three important points. First, we can quantify the value of additional evidence in terms of the health gain it is expected to generate. Second, we can quantify the health opportunity cost imposed by research expenditure. Third, the value of evidence generation depends on the budgetary policies in place for managing HIV resources under uncertainty. Generating evidence to reduce uncertainty is particularly valuable when decision makers are required to strictly adhere to expenditure plans and when transfers of funds across geographies/programmes are restricted.Conclusion: Better evidence can lead to health improvements in the same way as direct delivery of healthcare. Quantitative appraisals of evidence generation activities are important and should reflect the impact of improved evidence on population health, evidence generation costs and budgetary policies in place.
Eaton J, Grebe E, Baumler P, et al., 2018, Incidence Estimation Tools (inctools)
Tools for estimating incidence from biomarker data in cross-sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) doi:10.1097/EDE.0b013e3182576c07.
Marston M, Zaba B, Eaton J, 2018, Relative patterns of sexual activity and fertility among HIV positive and negative women – evidence from 46 DHS, PLoS ONE, Vol: 13, ISSN: 1932-6203
ObjectivesProjections of fertility of HIV positive women as ART scales up are needed to plan prevention of mother-to-child transmission (PMTCT) services. We describe differences in exposure to pregnancy between HIV positive and HIV negative women by age, region and national ART coverage to evaluate the extent to which behavioural differences explain lower fertility among HIV positive women and assess whether exposure to pregnancy has changed with antiretroviral treatment (ART) scale-up.MethodsWe analysed 46 nationally representative household surveys in sub-Saharan Africa conducted between 2003 and 2015 to estimate risk of exposure to recent sex and pregnancy of HIV positive and HIV negative women by age using a log binomial model. We tested for regional and urban/rural differences and associations with national ART coverage. We estimated an adjusted fertility rate ratio of HIV positive to HIV negative women adjusting for differences in exposure to pregnancy.ResultsExposure to pregnancy differs significantly between HIV positive and negative women by age, modified by region. Younger HIV positive women have a higher exposure to pregnancy than HIV negative women and the opposite is true at older ages. The switch occurs at 25–29 for rural women and 30–34 for urban women. There was no evidence that exposure to pregnancy of HIV positive women have changed as national ART coverage increased. The inferred rate of fecundity of HIV positive women when adjusted for differences in exposure to pregnancy were lower than unadjusted fertility rate ratios in women aged 20–29 and 20–24 in urban and rural areas respectively varying between 0.6 and 0.9 over regions.DiscussionThe direct effects of HIV on fertility are broadly similar across ages, while the dramatic age gradient that has frequently been observed is largely attributable to variation in relative sexual exposure by age.
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