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Kasaie P, Stewart C, Humes E, Gerace L, Hyle EP, Zalla LC, Rebeiro PF, Silverberg MJ, Rubtsova AA, Rich AJ, Gebo K, Lesko CR, Fojo AT, Lang R, Edwards JK, Althoff KN. Impact of subgroup-specific heterogeneities and dynamic changes in mortality rates on forecasted population size, deaths, and age distribution of persons receiving antiretroviral treatment in the United States: a computer simulation study. Ann Epidemiol 2023; 87:S1047-2797(23)00171-0. [PMID: 37741499 PMCID: PMC10841391 DOI: 10.1016/j.annepidem.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/06/2023] [Accepted: 09/14/2023] [Indexed: 09/25/2023]
Abstract
PURPOSE Model-based forecasts of population size, deaths, and age distribution of people with HIV (PWH) are helpful for public health and clinical services planning but are influenced by subgroup-specific heterogeneities and changes in mortality rates. METHODS Using an agent-based simulation of PWH in the United States, we examined the impact of distinct approaches to parametrizing mortality rates on forecasted epidemiology of PWH on antiretroviral treatment (ART). We first estimated mortality rates among (1) all PWH, (2) sex-specific, (3) sex-and-race/ethnicity-specific, and (4) sex-race/ethnicity-and-HIV-acquisition-risk-specific subgroups. We then assessed each scenario by (1) allowing unrestricted reductions in age-specific mortality rates over time and (2) restricting the mortality rates among PWH to subgroup-specific mortality thresholds from the general population. RESULTS Among the eight scenarios examined, those lacking subgroup-specific heterogeneities and those allowing unrestricted reductions in future mortality rates forecasted the lowest number of deaths among all PWH and 9 of the 15 subgroups through 2030. The forecasted overall number and age distribution of people with a history of injection drug use were sensitive to inclusion of subgroup-specific mortality rates. CONCLUSIONS Our results underscore the potential risk of underestimating future deaths by models lacking subgroup-specific heterogeneities in mortality rates, and those allowing unrestricted reductions in future mortality rates.
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Affiliation(s)
- Parastu Kasaie
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
| | - Cameron Stewart
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Elizabeth Humes
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Lucas Gerace
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Emily P Hyle
- Medical Practice Evaluation Center, Massachusetts General Hospital; Division of Infectious Diseases, Massachusetts General Hospital, Boston; Harvard Medical School, Boston
| | - Lauren C Zalla
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Peter F Rebeiro
- Department of Medicine & Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN
| | | | - Anna A Rubtsova
- Emory University Rollins School of Public Health, Department of Behavioral, Social, and Health Education Sciences, Atlanta, GA
| | - Ashleigh J Rich
- Department of Social Medicine, University of North Carolina at Chapel Hill, Chapel Hill
| | - Kelly Gebo
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Catherine R Lesko
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Anthony T Fojo
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Raynell Lang
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Jessie K Edwards
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Keri N Althoff
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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le Roux SM, Odayar J, Sutcliffe CG, Salvatore PP, de Broucker G, Dowdy D, McCann NC, Frank SC, Ciaranello AL, Myer L, Vojnov L. Cost-effectiveness of point-of-care versus centralised, laboratory-based nucleic acid testing for diagnosis of HIV in infants: a systematic review of modelling studies. Lancet HIV 2023; 10:e320-e331. [PMID: 37149292 PMCID: PMC10175481 DOI: 10.1016/s2352-3018(23)00029-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/31/2023] [Accepted: 02/03/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Point-of-care (POC) nucleic acid testing for diagnosis of HIV in infants facilitates earlier initiation of antiretroviral therapy (ART) than with centralised (standard-of-care, SOC) testing, but can be more expensive. We evaluated cost-effectiveness data from mathematical models comparing POC with SOC to provide global policy guidance. METHODS In this systematic review of modelling studies, we searched PubMed, MEDLINE, Embase, the National Health Service Economic Evaluation Database, Econlit, and conference abstracts, combining terms for "HIV" + "infant"/"early infant diagnosis" + "point-of-care" + "cost-effectiveness" + "mathematical models", without restrictions from database inception to July 15, 2022. We selected reports of mathematical cost-effectiveness models comparing POC with SOC for HIV diagnosis in infants younger than 18 months. Titles and abstracts were independently reviewed, with full-text review for qualifying articles. We extracted data on health and economic outcomes and incremental cost-effectiveness ratios (ICERs) for narrative synthesis. The primary outcomes of interest were ICERs (comparing POC with SOC) for ART initiation and survival of children living with HIV. FINDINGS Our search identified 75 records through database search. 13 duplicates were excluded, leaving 62 non-duplicate articles. 57 records were excluded and five were reviewed in full text. One article was excluded as it was not a modelling study, and four qualifying studies were included in the review. These four reports were from two mathematical models from two independent modelling groups. Two reports used the Johns Hopkins model to compare POC with SOC for repeat early infant diagnosis testing in the first 6 months in sub-Saharan Africa (first report, simulation of 25 000 children) and Zambia (second report, simulation of 7500 children). In the base scenario, POC versus SOC increased probability of ART initiation within 60 days of testing from 19% to 82% (ICER per additional ART initiation range US$430-1097; 9-month cost horizon) in the first report; and from 28% to 81% in the second ($23-1609, 5-year cost horizon). Two reports compared POC with SOC for testing at 6 weeks in Zimbabwe using the Cost-Effectiveness of Preventing AIDS Complications-Paediatric model (simulation of 30 million children; lifetime horizon). POC increased life expectancy and was considered cost-effective compared with SOC (ICER $711-850 per year of life saved in HIV-exposed children). Results were robust throughout sensitivity and scenario analyses. In most scenarios, platform cost-sharing (co-use with other programmes) resulted in POC being cost-saving compared with SOC. INTERPRETATION Four reports from two different models suggest that POC is a cost-effective and potentially cost-saving strategy for upscaling of early infant testing compared with SOC. FUNDING Bill & Melinda Gates Foundation, Unitaid, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, WHO, and Massachusetts General Hospital Research Scholars.
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Affiliation(s)
- Stanzi M le Roux
- Division of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Cape Town, South Africa.
| | - Jasantha Odayar
- Division of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Cape Town, South Africa
| | - Catherine G Sutcliffe
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Phillip P Salvatore
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gatien de Broucker
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - David Dowdy
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nicole C McCann
- Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital Boston, MA, USA
| | - Simone C Frank
- Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital Boston, MA, USA
| | - Andrea L Ciaranello
- Medical Practice Evaluation Center, Department of Medicine, Massachusetts General Hospital Boston, MA, USA; Division of Infectious Diseases, Massachusetts General Hospital Boston, MA, USA
| | - Landon Myer
- Division of Epidemiology & Biostatistics, School of Public Health, University of Cape Town, Cape Town, South Africa
| | - Lara Vojnov
- Global HIV, Hepatitis and STI Programme, World Health Organization, Geneva, Switzerland
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Zhong H, Brandeau ML, Yazdi GE, Wang J, Nolen S, Hagan L, Thompson WW, Assoumou SA, Linas BP, Salomon JA. Metamodeling for Policy Simulations with Multivariate Outcomes. Med Decis Making 2022; 42:872-884. [PMID: 35735216 PMCID: PMC9452454 DOI: 10.1177/0272989x221105079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PURPOSE Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes. METHODS We combine 2 algorithm adaptation methods-multitarget stacking and regression chain with maximum correlation-with different base learners including linear regression (LR), elastic net (EE) with second-order terms, Gaussian process regression (GPR), random forests (RFs), and neural networks. We optimize integrated models using variable selection and hyperparameter tuning. We compare the accuracy, efficiency, and interpretability of different approaches. As an example application, we develop metamodels to emulate a microsimulation model of testing and treatment strategies for hepatitis C in correctional settings. RESULTS Output variables from the simulation model were correlated (average ρ = 0.58). Without multioutput algorithm adaptation methods, in-sample fit (measured by R2) ranged from 0.881 for LR to 0.987 for GPR. The multioutput algorithm adaptation method increased R2 by an average 0.002 across base learners. Variable selection and hyperparameter tuning increased R2 by 0.009. Simpler models such as LR, EE, and RF required minimal training and prediction time. LR and EE had advantages in model interpretability, and we considered methods for improving the interpretability of other models. CONCLUSIONS In our example application, the choice of base learner had the largest impact on R2; multioutput algorithm adaptation and variable selection and hyperparameter tuning had a modest impact. Although advantages and disadvantages of specific learning algorithms may vary across different modeling applications, our framework for metamodeling in policy analyses with multivariate outcomes has broad applicability to decision analysis in health and medicine.
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Affiliation(s)
- Huaiyang Zhong
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Golnaz Eftekhari Yazdi
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Jianing Wang
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Shayla Nolen
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | | | - William W Thompson
- Division of Viral Hepatitis, Center for Disease Control and Prevention, Atlanta, GA, USA
| | - Sabrina A Assoumou
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Benjamin P Linas
- Section of Infectious Diseases, Department of Medicine, Boston Medical Center, Boston, MA, USA
| | - Joshua A Salomon
- Center for Health Policy and Center for Primary Care and Outcomes Research, Stanford University, Stanford, CA, USA
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Malloy GSP, Brandeau ML. When Is Mass Prophylaxis Cost-Effective for Epidemic Control? A Comparison of Decision Approaches. Med Decis Making 2022; 42:1052-1063. [PMID: 35591754 DOI: 10.1177/0272989x221098409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND For certain communicable disease outbreaks, mass prophylaxis of uninfected individuals can curtail new infections. When an outbreak emerges, decision makers could benefit from methods to quickly determine whether mass prophylaxis is cost-effective. We consider 2 approaches: a simple decision model and machine learning meta-models. The motivating example is plague in Madagascar. METHODS We use a susceptible-exposed-infectious-removed (SEIR) epidemic model to derive a decision rule based on the fraction of the population infected, effective reproduction ratio, infection fatality rate, quality-adjusted life-year loss associated with death, prophylaxis effectiveness and cost, time horizon, and willingness-to-pay threshold. We also develop machine learning meta-models of a detailed model of plague in Madagascar using logistic regression, random forest, and neural network models. In numerical experiments, we compare results using the decision rule and the meta-models to results obtained using the simulation model. We vary the initial fraction of the population infected, the effective reproduction ratio, the intervention start date and duration, and the cost of prophylaxis. LIMITATIONS We assume homogeneous mixing and no negative side effects due to antibiotic prophylaxis. RESULTS The simple decision rule matched the SEIR model outcome in 85.4% of scenarios. Using data for a 2017 plague outbreak in Madagascar, the decision rule correctly indicated that mass prophylaxis was not cost-effective. The meta-models were significantly more accurate, with an accuracy of 92.8% for logistic regression, 95.8% for the neural network model, and 96.9% for the random forest model. CONCLUSIONS A simple decision rule using minimal information about an outbreak can accurately evaluate the cost-effectiveness of mass prophylaxis for outbreak mitigation. Meta-models of a complex disease simulation can achieve higher accuracy but with greater computational and data requirements and less interpretability. HIGHLIGHTS We use a susceptible-exposed-infectious-removed model and net monetary benefit to derive a simple decision rule to evaluate the cost-effectiveness of mass prophylaxis.We use the example of plague in Madagascar to compare the performance of the analytically derived decision rule to that of machine learning meta-models trained on a stochastic dynamic transmission model.We assess the accuracy of each approach for different combinations of disease dynamics and intervention scenarios.The machine learning meta-models are more accurate predictors of mass prophylaxis cost-effectiveness. However, the simple decision rule is also accurate and may be a preferred substitute in low-resource settings.
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Affiliation(s)
- Giovanni S P Malloy
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Margaret L Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Gutiérrez JP, Trossero A. Socioeconomic inequalities in HIV knowledge, HIV testing, and condom use among adolescent and young women in Latin America and the Caribbean. Rev Panam Salud Publica 2021; 45:e47. [PMID: 34054931 PMCID: PMC8147735 DOI: 10.26633/rpsp.2021.47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/17/2021] [Indexed: 12/04/2022] Open
Abstract
Objective. To appraise the presence and magnitude of inter- and intra-country health inequalities related to HIV in Latin America and the Caribbean (LAC) among young females. Methods. We analyzed household surveys in twenty LAC countries, that included data from female adolescents and young women (ages 15-24) between 2008 and 2018, measuring inequality with the concentration index of 4 indicators: 1) whether individuals have heard of HIV or not, 2) a composite variable of correct knowledge, 3) reported condom use with the last partner, and 4) whether individuals were ever tested for HIV. Results. Participants from households in countries with higher socioeconomic status are more likely to have heard of HIV, have correct knowledge of HIV transmission, and have used condoms during their last sexual intercourse. The inter-country concentration index for those indicators were 0.352, 0.302 and 0.110, respectively. Conclusions. Economically disadvantaged female adolescents and young women in LAC face an increased risk for HIV, as they are less aware of HIV and its actual transmission mechanism and are less likely to use condoms with their sexual partners. There is an urgent need to tailor prevention strategies of sexually transmitted infections and HIV for adolescents and young women that are sensitive to their socioeconomic context.
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Affiliation(s)
- Juan Pablo Gutiérrez
- National Autonomous University of Mexico (UNAM) Mexico City Mexico National Autonomous University of Mexico (UNAM), Mexico City, Mexico
| | - Alejandra Trossero
- UNICEF Regional Office for Latin America and the Caribbean Panama Panama UNICEF Regional Office for Latin America and the Caribbean, Panama, Panama
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Desmonde S, Ciaranello AL, Malateste K, Musick B, Patten G, Vu AT, Edmonds A, Neilan AM, Duda SN, Wools-Kaloustian K, Davies MA, Leroy V. Age-specific mortality rate ratios in adolescents and youth aged 10-24 years living with perinatally versus nonperinatally acquired HIV. AIDS 2021; 35:625-632. [PMID: 33252479 PMCID: PMC7904586 DOI: 10.1097/qad.0000000000002765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/03/2020] [Accepted: 11/09/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To measure mortality incidence rates and incidence rate ratios (IRR) in adolescents and youth living with perinatally acquired HIV (YPHIV) compared with those living with nonperinatally acquired HIV (YNPHIV), by region, by sex, and during the ages of 10-14, 15-19, and 20-24 years in IeDEA. DESIGN AND METHODS All those with a confirmed HIV diagnosis, antiretroviral therapy (ART)-naive at enrollment, and who have post-ART follow-up while aged 10-24 years between 2004 and 2016 were included. We estimated post-ART mortality incidence rates and 95% confidence intervals (95% CI) per 100 person-years for YPHIV (enrolled into care <10 years of age) and YNPHIV (enrolled ≥10 years and <25 years). We estimate mortality IRRs in a negative binomial regression model, adjusted for sex, region time-varying age, CD4+ cell count at ART initiation (<350 cells/μl, ≥350 cells/μl, unknown), and time on ART (<12 and ≥12 months). RESULTS Overall, 104 846 adolescents and youth were included: 21 340 (20%) YPHIV (50% women) and 83 506 YNPHIV (80% women). Overall mortality incidence ratios were higher among YNPHIV (incidence ratio: 2.3/100 person-years; 95% CI: 2.2-2.4) compared with YPHIV (incidence ratio: 0.7/100 person-years; 95% CI: 0.7-0.8). Among adolescents aged 10-19 years, mortality was lower among YPHIV compared with YNPHIV (all IRRs <1, ranging from 0.26, 95% CI: 0.13-0.49 in 10-14-year-old boys in the Asia-Pacific to 0.51, 95% CI: 0.30-0.87 in 15-19-year-old boys in West Africa). CONCLUSION We report substantial amount of deaths occurring during adolescence. Mortality was significantly higher among YNPHIV compared to YPHIV. Specific interventions including HIV testing and early engagement in care are urgently needed to improve survival among YNPHIV.
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Affiliation(s)
- Sophie Desmonde
- Inserm U1027, Université Paul Sabatier Toulouse 3, Toulouse, France
| | - Andrea L. Ciaranello
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Karen Malateste
- Inserm U1219
- Bordeaux Population Health Center, Université de Bordeaux, Bordeaux, France
| | - Beverly Musick
- School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Gabriela Patten
- School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - An Thien Vu
- Children's Hospital 2, Ho Chi Minh City, Vietnam
| | - Andrew Edmonds
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Anne M. Neilan
- Division of General Academic Pediatrics, Department of Pediatrics, Massachusetts General Hospital for Children, Boston, Massachusetts
| | - Stephany N. Duda
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | | | - Mary-Ann Davies
- School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Valériane Leroy
- Inserm U1027, Université Paul Sabatier Toulouse 3, Toulouse, France
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Dunning L, Gandhi AR, Penazzato M, Soeteman DI, Revill P, Frank S, Phillips A, Dugdale C, Abrams E, Weinstein MC, Newell M, Collins IJ, Doherty M, Vojnov L, Fassinou Ekouévi P, Myer L, Mushavi A, Freedberg KA, Ciaranello AL. Optimizing infant HIV diagnosis with additional screening at immunization clinics in three sub-Saharan African settings: a cost-effectiveness analysis. J Int AIDS Soc 2021; 24:e25651. [PMID: 33474817 PMCID: PMC8992471 DOI: 10.1002/jia2.25651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 10/19/2020] [Accepted: 11/17/2020] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION Uptake of early infant HIV diagnosis (EID) varies widely across sub-Saharan African settings. We evaluated the potential clinical impact and cost-effectiveness of universal maternal HIV screening at infant immunization visits, with referral to EID and maternal antiretroviral therapy (ART) initiation. METHODS Using the CEPAC-Pediatric model, we compared two strategies for infants born in 2017 in Côte d'Ivoire (CI), South Africa (SA), and Zimbabwe: (1) existing EID programmes offering six-week nucleic acid testing (NAT) for infants with known HIV exposure (EID), and (2) EID plus universal maternal HIV screening at six-week infant immunization visits, leading to referral for infant NAT and maternal ART initiation (screen-and-test). Model inputs included published Ivoirian/South African/Zimbabwean data: maternal HIV prevalence (4.8/30.8/16.1%), current uptake of EID (40/95/65%) and six-week immunization attendance (99/74/94%). Referral rates for infant NAT and maternal ART initiation after screen-and-test were 80%. Costs included NAT ($24/infant), maternal screening ($10/mother-infant pair), ART ($5 to 31/month) and HIV care ($15 to 190/month). Model outcomes included mother-to-child transmission of HIV (MTCT) among HIV-exposed infants, and life expectancy (LE) and mean lifetime per-person costs for children with HIV (CWH) and all children born in 2017. We calculated incremental cost-effectiveness ratios (ICERs) using discounted (3%/year) lifetime costs and LE for all children. We considered two cost-effectiveness thresholds in each country: (1) the per-capita GDP ($1720/6380/2150) per year-of-life saved (YLS), and (2) the CEPAC-generated ICER of offering 2 versus 1 lifetime ART regimens (e.g. offering second-line ART; $520/500/580/YLS). RESULTS With EID, projected six-week MTCT was 9.3% (CI), 4.2% (SA) and 5.2% (Zimbabwe). Screen-and-test decreased total MTCT by 0.2% to 0.5%, improved LE by 2.0 to 3.5 years for CWH and 0.03 to 0.07 years for all children, and increased discounted costs by $17 to 22/child (all children). The ICER of screen-and-test compared to EID was $1340/YLS (CI), $650/YLS (SA) and $670/YLS (Zimbabwe), below the per-capita GDP but above the ICER of 2 versus 1 lifetime ART regimens in all countries. CONCLUSIONS Universal maternal HIV screening at immunization visits with referral to EID and maternal ART initiation may reduce MTCT, improve paediatric LE, and be of comparable value to current HIV-related interventions in high maternal HIV prevalence settings like SA and Zimbabwe.
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Affiliation(s)
- Lorna Dunning
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
| | - Aditya R Gandhi
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
| | - Martina Penazzato
- Global HIV, Hepatitis, and STIs ProgrammeWorld Health OrganizationGenevaSwitzerland
| | - Djøra I Soeteman
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
- Center for Health Decision ScienceHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Paul Revill
- Center for Health EconomicsUniversity of YorkYorkUnited Kingdom
| | - Simone Frank
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
| | - Andrew Phillips
- Institute for Global HealthUniversity College LondonLondonUnited Kingdom
| | - Caitlin Dugdale
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
- Division of Infectious DiseasesMassachusetts General HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Elaine Abrams
- Mailman School of Public HealthICAP at Columbia UniversityNew York CityNYUSA
| | - Milton C Weinstein
- Center for Health Decision ScienceHarvard T.H. Chan School of Public HealthBostonMAUSA
| | - Marie‐Louise Newell
- Institute for Development StudiesHuman Development and HealthFaculty of MedicineUniversity of SouthamptonSouthamptonUnited Kingdom
- School of Public HealthFaculty of Health SciencesUniversity of WitwatersrandJohannesburgSouth Africa
| | - Intira J Collins
- Medical Research Council Clinical Trials UnitUniversity College LondonLondonUnited Kingdom
| | - Meg Doherty
- Global HIV, Hepatitis, and STIs ProgrammeWorld Health OrganizationGenevaSwitzerland
| | - Lara Vojnov
- Global HIV, Hepatitis, and STIs ProgrammeWorld Health OrganizationGenevaSwitzerland
| | | | - Landon Myer
- Division of Epidemiology & BiostatisticsSchool of Public Health & Family MedicineUniversity of Cape TownCape TownSouth Africa
| | | | - Kenneth A Freedberg
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
- Division of Infectious DiseasesMassachusetts General HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
| | - Andrea L Ciaranello
- Medical Practice Evaluation CenterMassachusetts General HospitalBostonMAUSA
- Division of Infectious DiseasesMassachusetts General HospitalBostonMAUSA
- Harvard Medical SchoolBostonMAUSA
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Jalali MS, Botticelli M, Hwang RC, Koh HK, McHugh RK. The opioid crisis: need for systems science research. Health Res Policy Syst 2020; 18:88. [PMID: 32771004 PMCID: PMC7414582 DOI: 10.1186/s12961-020-00598-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/29/2020] [Indexed: 01/07/2023] Open
Abstract
The opioid epidemic in the United States has had a devastating impact on millions of people as well as on their families and communities. The increased prevalence of opioid misuse, use disorder and overdose in recent years has highlighted the need for improved public health approaches for reducing the tremendous harms of this illness. In this paper, we explain and call for the need for more systems science approaches, which can uncover the complexities of the opioid crisis, and help evaluate, analyse and forecast the effectiveness of ongoing and new policy interventions. Similar to how a stream of systems science research helped policy development in infectious diseases and obesity, more systems science research is needed in opioids.
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Affiliation(s)
- Mohammad S. Jalali
- grid.38142.3c000000041936754XMGH Institute for Technology Assessment, Harvard Medical School, 101 Merrimac St, Suite 1010, Boston, MA 02114 United States of America ,grid.116068.80000 0001 2341 2786MIT Sloan School of Management, 100 Main St, Cambridge, MA 02142 United States of America
| | - Michael Botticelli
- grid.239424.a0000 0001 2183 6745Grayken Center for Addiction, Boston Medical Center, Boston, MA United States of America
| | - Rachael C. Hwang
- grid.116068.80000 0001 2341 2786MIT Sloan School of Management, 100 Main St, Cambridge, MA 02142 United States of America
| | - Howard K. Koh
- grid.38142.3c000000041936754XT.H. Chan School of Public Health, Harvard
University, Boston, MA United States of America ,grid.38142.3c000000041936754XHarvard Kennedy School, Harvard University, Cambridge, MA United States of America
| | - R. Kathryn McHugh
- grid.38142.3c000000041936754XMGH Institute for Technology Assessment, Harvard Medical School, 101 Merrimac St, Suite 1010, Boston, MA 02114 United States of America ,grid.240206.20000 0000 8795 072XDivision of Alcohol and Drug Abuse, McLean Hospital, Belmont, MA United States of America
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10
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Neilan AM, Patel K, Agwu AL, Bassett IV, Amico KR, Crespi CM, Gaur AH, Horvath KJ, Powers KA, Rendina HJ, Hightow-Weidman LB, Li X, Naar S, Nachman S, Parsons JT, Simpson KN, Stanton BF, Freedberg KA, Bangs AC, Hudgens MG, Ciaranello AL. Model-Based Methods to Translate Adolescent Medicine Trials Network for HIV/AIDS Interventions Findings Into Policy Recommendations: Rationale and Protocol for a Modeling Core (ATN 161). JMIR Res Protoc 2019; 8:e9898. [PMID: 30990464 PMCID: PMC6488956 DOI: 10.2196/resprot.9898] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 03/04/2019] [Accepted: 03/05/2019] [Indexed: 12/12/2022] Open
Abstract
Background The United States Centers for Disease Control and Prevention estimates that approximately 60,000 US youth are living with HIV. US youth living with HIV (YLWH) have poorer outcomes compared with adults, including lower rates of diagnosis, engagement, retention, and virologic suppression. With Adolescent Medicine Trials Network for HIV/AIDS Interventions (ATN) support, new trials of youth-centered interventions to improve retention in care and medication adherence among YLWH are underway. Objective This study aimed to use a computer simulation model, the Cost-Effectiveness of Preventing AIDS Complications (CEPAC)-Adolescent Model, to evaluate selected ongoing and forthcoming ATN interventions to improve viral load suppression among YLWH and to define the benchmarks for uptake, effectiveness, durability of effect, and cost that will make these interventions clinically beneficial and cost-effective. Methods This protocol, ATN 161, establishes the ATN Modeling Core. The Modeling Core leverages extensive data—already collected by successfully completed National Institutes of Health–supported studies—to develop novel approaches for modeling critical components of HIV disease and care in YLWH. As new data emerge from ongoing ATN trials during the award period about the effectiveness of novel interventions, the CEPAC-Adolescent simulation model will serve as a flexible tool to project their long-term clinical impact and cost-effectiveness. The Modeling Core will derive model input parameters and create a model structure that reflects key aspects of HIV acquisition, progression, and treatment in YLWH. The ATN Modeling Core Steering Committee, with guidance from ATN leadership and scientific experts, will select and prioritize specific model-based analyses as well as provide feedback on derivation of model input parameters and model assumptions. Project-specific teams will help frame research questions for model-based analyses as well as provide feedback regarding project-specific inputs, results, sensitivity analyses, and policy conclusions. Results This project was funded as of September 2017. Conclusions The ATN Modeling Core will provide critical information to guide the scale-up of ATN interventions and the translation of ATN data into policy recommendations for YLWH in the United States.
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Affiliation(s)
- Anne M Neilan
- Division of General Academic Pediatrics, Massachusetts General Hospital, Boston, MA, United States.,Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, United States
| | - Kunjal Patel
- Department of Epidemiology and Center for Biostatistics in AIDS Research, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Allison L Agwu
- Departments of Pediatric and Adult Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ingrid V Bassett
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, United States.,Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - K Rivet Amico
- University of Michigan School of Public Health, Ann Arbor, MI, United States
| | - Catherine M Crespi
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, United States
| | - Aditya H Gaur
- St. Jude's Children's Research Hospital, Memphis, TN, United States
| | - Keith J Horvath
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Kimberly A Powers
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - H Jonathon Rendina
- Hunter College of the City University of New York, New York, NY, United States
| | - Lisa B Hightow-Weidman
- Institute for Global Health & Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Xiaoming Li
- Arnold School of Public Health, University of South Carolina, Columbia, SC, United States
| | - Sylvie Naar
- Center for Translational Behavioral Research, Florida State University, Tallahassee, FL, United States
| | - Sharon Nachman
- State University of New York, Stony Brook, NY, United States
| | - Jeffrey T Parsons
- Hunter College of the City University of New York, New York, NY, United States
| | - Kit N Simpson
- Medical University of South Carolina, Charleston, SC, United States
| | - Bonita F Stanton
- Hackensack Meridian School of Medicine at Seton Hall University, Nutley, NJ, United States
| | - Kenneth A Freedberg
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, United States.,Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, United States.,Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Audrey C Bangs
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, United States
| | - Michael G Hudgens
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Andrea L Ciaranello
- Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, MA, United States.,Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
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