A framework to model global, regional, and national estimates of intimate partner violence.

BACKGROUND: Accurate and reliable estimates of violence against women form the backbone of global and regional monitoring efforts to eliminate this human right violation and public health problem. Estimating the prevalence of intimate partner violence (IPV) is challenging due to variations in case definition and recall period, surveyed populations, partner definition, level of age disaggregation, and survey representativeness, among others. In this paper, we aim to develop a sound and flexible statistical modeling framework for global, regional, and national IPV statistics. METHODS: We modeled IPV within a Bayesian multilevel modeling framework, accounting for heterogeneity of age groups using age-standardization, and age patterns and time trends using splines functions. Survey comparability is achieved using adjustment factors which are estimated using exact matching and their uncertainty accounted for. Both in-sample and out-of-sample comparisons are used for model validation, including posterior predictive checks. Post-processing of models' outputs is performed to aggregate estimates at different geographic levels and age groups. RESULTS: A total of 307 unique studies conducted between 2000-2018, from 154 countries/areas, and totaling nearly 1.8 million unique women responses informed lifetime IPV. Past year IPV had a similar number of studies (n = 332), countries/areas represented (n = 159), and individual responses (n = 1.8 million). Roughly half of IPV observations required some adjustments. Posterior predictive checks suggest good model fit to data and out-of-sample comparisons provided reassuring results with small median prediction errors and appropriate coverage of predictions' intervals. CONCLUSIONS: The proposed modeling framework can pool both national and sub-national surveys, account for heterogeneous age groups and age trends, accommodate different surveyed populations, adjust for differences in survey instruments, and efficiently propagate uncertainty to model outputs. Describing this model to reproducible levels of detail enables the accurate interpretation and responsible use of estimates to inform effective violence against women prevention policy and programs, and global monitoring of elimination efforts as part of the Sustainable Development Goals.

Canada's provincial COVID-19 pandemic modelling efforts: A review of mathematical models and their impacts on the responses.

SETTING: Mathematical modelling played an important role in the public health response to COVID-19 in Canada. Variability in epidemic trajectories, modelling approaches, and data infrastructure across provinces provides a unique opportunity to understand the factors that shaped modelling strategies. INTERVENTION: Provinces implemented stringent pandemic interventions to mitigate SARS-CoV-2 transmission, considering evidence from epidemic models. This study aimed to summarize provincial COVID-19 modelling efforts. We identified modelling teams working with provincial decision-makers, through referrals and membership in Canadian modelling networks. Information on models, data sources, and knowledge translation were abstracted using standardized instruments. OUTCOMES: We obtained information from six provinces. For provinces with sustained community transmission, initial modelling efforts focused on projecting epidemic trajectories and healthcare demands, and evaluating impacts of proposed interventions. In provinces with low community transmission, models emphasized quantifying importation risks. Most of the models were compartmental and deterministic, with projection horizons of a few weeks. Models were updated regularly or replaced by new ones, adapting to changing local epidemic dynamics, pathogen characteristics, vaccines, and requests from public health. Surveillance datasets for cases, hospitalizations and deaths, and serological studies were the main data sources for model calibration. Access to data for modelling and the structure for knowledge translation differed markedly between provinces. IMPLICATION: Provincial modelling efforts during the COVID-19 pandemic were tailored to local contexts and modulated by available resources. Strengthening Canadian modelling capacity, developing and sustaining collaborations between modellers and governments, and ensuring earlier access to linked and timely surveillance data could help improve pandemic preparedness.

RéSUMé: CONTEXTE: La modélisation mathématique a joué un rôle de premier plan dans les ripostes sanitaires à la COVID-19 au Canada. Les différentes trajectoires épidémiques provinciales, leurs approches de modélisation et infrastructures de données représentent une occasion unique de comprendre les facteurs qui ont influencé les stratégies de modélisation provinciales. INTERVENTION: Les provinces ont mis en place des mesures de santé publique strictes afin d'atténuer la transmission du SRAS-CoV-2 en tenant compte des données probantes provenant des modèles épidémiques. Notre étude vise à décrire et résumer les efforts provinciaux de modélisation de la COVID-19. Nous avons identifié les équipes de modélisation travaillant avec les décideurs provinciaux parmi les réseaux Canadiens de modélisation et par référence. Les informations sur les modèles, leurs sources de données et les approches de mobilisation des connaissances ont été obtenues à l'aide d'instruments standardisés. RéSULTATS: Nous avons colligé les informations provenant de six provinces. Pour les provinces qui ont eu de la transmission communautaire soutenue, les efforts de modélisation initiaux se sont concentrés sur la projection des trajectoires épidémiques et des demandes de soins de santé et sur l'évaluation des impacts des interventions proposées. Dans les provinces où la transmission communautaire a été faible, les modèles visaient à quantifier les risques d'importation. La plupart des équipes ont développé des modèles à compartiments déterministes avec des horizons de projection de quelques semaines. Les modèles ont été régulièrement mis à jour ou remplacés par de nouveaux, s'adaptant aux dynamiques locales, à l'arrivée de nouveaux variants, aux vaccins et aux demandes des autorités de santé publique. Les données de surveillance des cas, des hospitalisations et des décès, ainsi que les études sérologiques, ont constitué les principales sources de données pour calibrer les modèles. L'accès aux données pour la modélisation et la structure de mobilisation des connaissances différaient considérablement d'une province à l'autre. IMPLICATION: Les efforts de modélisation provinciaux pendant la pandémie de la COVID-19 ont été adaptés aux contextes locaux et modulés par les ressources disponibles. Le renforcement de la capacité canadienne de modélisation, le développement et le maintien de collaborations entre les modélisateurs et les gouvernements, ainsi qu'un accès rapide et opportun aux données de surveillance individuelles et liées pourraient contribuer à améliorer la préparation aux futures pandémies.

Public health interventions, priority populations, and the impact of COVID-19 disruptions on hepatitis C elimination among people who have injected drugs in Montreal (Canada): A modeling study.

BACKGROUND: In Montreal (Canada), high hepatitis C virus (HCV) seroincidence (21 per 100 person-years in 2017) persists among people who have injected drugs (PWID) despite relatively high testing rates and coverage of needle and syringe programs (NSP) and opioid agonist therapy (OAT). We assessed the potential of interventions to achieve HCV elimination (80% incidence reduction and 65% reduction in HCV-related mortality between 2015 and 2030) in the context of COVID-19 disruptions among all PWID and PWID living with HIV. METHODS: Using a dynamic model of HCV-HIV co-transmission, we simulated increases in NSP (from 82% to 95%) and OAT (from 33% to 40%) coverage, HCV testing (every 6 months), or treatment rate (100 per 100 person-years) starting in 2022 among all PWID and PWID living with HIV. We also modeled treatment scale-up among active PWID only (i.e., people who report injecting in the past six months). We reduced intervention levels in 2020-2021 due to COVID-19-related disruptions. Outcomes included HCV incidence, prevalence, and mortality, and proportions of averted chronic HCV infections and deaths. RESULTS: COVID-19-related disruptions could have caused temporary rebounds in HCV transmission. Further increasing NSP/OAT or HCV testing had little impact on incidence. Scaling-up treatment among all PWID achieved incidence and mortality targets among all PWID and PWID living with HIV. Focusing treatment on active PWID could achieve elimination, yet fewer projected deaths were averted (36% versus 48%). CONCLUSIONS: HCV treatment scale-up among all PWID will be required to eliminate HCV in high-incidence and prevalence settings. Achieving elimination by 2030 will entail concerted efforts to restore and enhance pre-pandemic levels of HCV prevention and care.

Implementing opt-out hepatitis C virus (HCV) screening in Canadian provincial prisons: A model-based cost-effectiveness analysis.

BACKGROUND: Implementing opt-out hepatitis C virus (HCV) screening across Canadian provincial prisons is crucial to achieving micro-elimination. Given short incarceration lengths, the most cost-effective screening strategy remains unknown. We compared the cost-effectiveness of current standard of care (venipuncture-based HCV-antibody+HCV RNA) and 13 alternative strategies in Quebec's largest provincial prison. METHODS: A prison cohort was simulated with a Markov micro-simulation model. Strategies differed in the biomarkers, sampling methods, and number of tests used. The model considered incarceration lengths, time to linkage to care (LTC), nursing costs, and tests' costs, performances, acceptability and turnaround times. Outcomes included costs (Canadian dollars, CAD$), number of true positives linked to care, and incremental cost-effectiveness ratios (ICERs, additional $/additional TP-L). A one-year time horizon and health-payer perspective were adopted. RESULTS: Across all analyses, three strategies consistently provided the best value for money: venipuncture-based HCV-antibody+HCV-core antigen, venipuncture-based HCV-core antigen (base-case ICER=~ $720), and point-of-care HCV-antibody+HCV RNA (base-case ICER=$4,310). However, these strategies linked only 23%-29% viremic individuals to care. Main drivers of cost-effectiveness were the seroprevalence, proportion viremic, and time to LTC. CONCLUSION: Alternative strategies would be more cost-effective than standard of care for implementing opt-out screening in provincial prisons. However, interventions to maximize LTC should be explored.

The role of prison-based interventions for hepatitis C virus (HCV) micro-elimination among people who inject drugs in Montréal, Canada.

BACKGROUND: In Canada, hepatitis C virus (HCV) transmission primarily occurs among people who inject drugs (PWID) and people with experience in the prison system bare a disproportionate disease burden. These overlapping groups of individuals have been identified as priority populations for HCV micro-elimination in Canada, which is currently not on track to achieve its elimination targets. Considering the missed opportunities to intervene in provincial prisons, this study aims to estimate the population-level impact of prison-based interventions and post-release risk reduction strategies on HCV transmission among PWID in Montréal, a Canadian city with high HCV burden. METHODS: A dynamic HCV transmission model among PWID was developed and calibrated to community and prison bio-behavioural surveys in Montréal. Then, the relative impact of prison-based testing and treatment or post-release linkage to care (both 90% testing and 75% treatment coverage), alone or in combination with strategies that reduce the heightened post-release transmission risk by 50%, was estimated from 2018 to 2030, and compared to counterfactual scenarios. RESULTS: Prison-based test-and-treat strategies could lead to the greatest declines in incidence (48%; 95%CrI: 38-57%) over 2018-2030 and prevent the most new first chronic infections (22%; 95%CrI: 16-28%) among people never exposed to HCV. Prison testing and post-release linkage to care lead to a slightly lower decrease in incidence and prevented fraction of new chronic infections. Combining test-and-treat with risk reduction measures could further its epidemiological impact, preventing 35% (95%CrI: 29-40%) of new first chronic infections. When implemented concomitantly with community-based treatment scale-up, prison-based interventions had synergistic effects, averting a higher fraction of new first chronic infections. CONCLUSION: Offering HCV testing and treatment in provincial prisons, where incarcerations are frequent and sentences short, could change the course of the HCV epidemic in Montréal. Prison-based interventions with potential integration of post-release risk reduction measures should be considered as an integral part of HCV micro-elimination strategies in this setting.

Can hepatitis C elimination targets be sustained among people who inject drugs post-2030?

BACKGROUND: In high-income countries, people who inject drugs (PWID) are a priority population for eliminating hepatitis C virus (HCV) by 2030. Despite evidence informing micro-elimination strategies, little is known regarding efforts needed to maintain elimination targets in populations with ongoing acquisition risks. This model-based study investigates post-elimination transmission dynamics of HCV and HIV among PWID under different scenarios where harm reduction interventions and HCV testing and treatment are scaled-down. METHODS: We calibrated a dynamic compartmental model of concurrent HCV and HIV transmission among PWID in Montréal (Canada) to epidemiological data (2003-2018). We then simulated achieving the World Health Organization elimination targets by 2030. Finally, we assessed the impact of four post-elimination scenarios (2030-2050): 1) scaling-down testing, treatment, opioid agonist therapy (OAT), and needle and syringe programs (NSP) to pre-2020 levels; 2) only scaling-down testing and treatment; 3) suspending testing and treatment, while scaling down OAT and NSP to pre-2020 levels; 4) suspending testing and treatment and maintaining OAT and NSP coverage required for elimination. RESULTS: Scaling down interventions to pre-2020 levels (scenario 1) leads to a modest rebound in chronic HCV incidence from 2.4 to 3.6 per 100 person-years by 2050 (95% credible interval - CrI: 0.8-7.2). When only scaling down testing and treatment (scenario 2), chronic HCV incidence continues to decrease. In scenario 3 (suspending treatment and scaling down OAT and NSP), HCV incidence and mortality rapidly increase to 11.4 per 100 person-years (95%CrI: 7.4-15.5) and 3.2 per 1000 person-years (95%CrI: 2.4-4.0), respectively. HCV resurgence was mitigated in scenario 4 (maintaining OAT and NSP) as compared to scenario 3. All scenarios lead to decreases in the proportion of reinfections among incident cases and have little impact on HIV incidence and HIV-HCV coinfection prevalence. CONCLUSION: Despite ongoing transmission risks, HCV incidence rebounds slowly after 2030 under pre-2020 testing and treatment levels. This is heightened by maintaining high-coverage harm reduction interventions. Overall, sustaining elimination would require considerably less effort than achieving it.

Inferring population HIV incidence trends from surveillance data of recent HIV infection among HIV testing clients.

BACKGROUND: Measuring recent HIV infections from routine surveillance systems could allow timely and granular monitoring of HIV incidence patterns. We evaluated the relationship of two recent infection indicators with alternative denominators to true incidence patterns. METHODS: We used a mathematical model of HIV testing behaviours, calibrated to population-based surveys and HIV testing services programme data, to estimate the number of recent infections diagnosed annually from 2010 to 2019 in Côte d'Ivoire, Malawi, and Mozambique. We compared two different denominators to interpret recency data: those at risk of HIV acquisition (HIV-negative tests and recent infections) and all people testing HIV positive. Sex and age-specific longitudinal trends in both interpretations were then compared with modelled trends in HIV incidence, testing efforts and HIV positivity among HIV testing services clients. RESULTS: Over 2010-2019, the annual proportion of the eligible population tested increased in all countries, while positivity decreased. The proportion of recent infections among those at risk of HIV acquisition decreased, similar to declines in HIV incidence among adults (≥15 years old). Conversely, the proportion of recent infections among HIV-positive tests increased. The female-to-male ratio of the proportion testing recent among those at risk was closer to 1 than the true incidence sex ratio. CONCLUSION: The proportion of recent infections among those at risk of HIV acquisition is more indicative of HIV incidence than the proportion among HIV-positive tests. However, interpreting the observed patterns as surrogate measures for incidence patterns may still be confounded by different HIV testing rates between population groups or over time.

Accuracy of self-reported HIV-testing history and awareness of HIV-positive status in four sub-Saharan African countries.

BACKGROUND: In many countries in sub-Saharan Africa, self-reported HIV testing history and awareness of HIV-positive status from household surveys are used to estimate the percentage of people living with HIV (PLHIV) who know their HIV status. Despite widespread use, there is limited empirical information on the sensitivity of those self-reports, which can be affected by nondisclosure. METHODS: Bayesian latent class models were used to estimate the sensitivity of self-reported HIV-testing history and awareness of HIV-positive status in four Population-based HIV Impact Assessment surveys in Eswatini, Malawi, Tanzania, and Zambia. Antiretroviral (ARV) metabolite biomarkers were used to identify persons on treatment who did not accurately report their status. For those without ARV biomarkers, we used a pooled estimate of nondisclosure among untreated persons that was 1.48 higher than those on treatment. RESULTS: Among PLHIV, the model-estimated sensitivity of self-reported HIV-testing history ranged from 96% to 99% across surveys. The model-estimated sensitivity of self-reported awareness of HIV status varied from 91% to 97%. Nondisclosure was generally higher among men and those aged 15-24 years. Adjustments for imperfect sensitivity did not substantially influence estimates of PLHIV ever tested (difference <4%) but the proportion of PLHIV aware of their HIV-positive status was higher than the unadjusted proportion (difference <8%). CONCLUSION: Self-reported HIV-testing histories in four Eastern and Southern African countries are generally robust although adjustment for nondisclosure increases estimated awareness of status. These findings can contribute to further refinements in methods for monitoring progress along the HIV testing and treatment cascade.

The role of case importation in explaining differences in early SARS-CoV-2 transmission dynamics in Canada-A mathematical modeling study of surveillance data.

OBJECTIVE: The North American coronavirus disease-2019 (COVID-19) epidemic exhibited distinct early trajectories. In Canada, Quebec had the highest COVID-19 burden and its earlier March school break, taking place two weeks before those in other provinces, could have shaped early transmission dynamics. METHODS: We combined a semi-mechanistic model of SARS-CoV-2 transmission with detailed surveillance data from Quebec and Ontario (initially accounting for 85% of Canadian cases) to explore the impact of case importation and timing of control measures on cumulative hospitalizations. RESULTS: A total of 1544 and 1150 cases among returning travelers were laboratory-confirmed in Quebec and Ontario, respectively (symptoms onset ≤03-25-2020). Hospitalizations could have been reduced by 55% (95% CrI: 51%-59%) if no cases had been imported after Quebec's March break. However, if Quebec had experienced Ontario's number of introductions, hospitalizations would have only been reduced by 12% (95% CrI: 8%-16%). Early public health measures mitigated the epidemic spread as a one-week delay could have resulted in twice as many hospitalizations (95% CrI: 1.7-2.1). CONCLUSION: Beyond introductions, factors such as public health preparedness, responses and capacity could play a role in explaining interprovincial differences. In a context where regions are considering lifting travel restrictions, coordinated strategies and proactive measures are to be considered.

National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the 'first 90' from program and survey data.

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 'Shiny90') 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. Shiny90 provides estimates of HIV testing history, diagnosis rates, and knowledge of HIV status by age and sex. We validate Shiny90 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 Shiny90 can accurately reproduce longitudinal sex-specific trends in HIV testing. Out-of-sample predictions of the fraction of people living with HIV 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 of status 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 model's predictions of knowledge of status are higher than available self-reported HIV awareness estimates, however, suggesting - in line with previous studies - that these self-reports could be affected by nondisclosure of HIV status awareness. CONCLUSION: Knowledge of HIV status is a key indicator to monitor progress, identify bottlenecks, and target HIV responses. Shiny90 can help countries track progress towards their 'first 90' by leveraging surveys of HIV testing behaviors and annual HTS program data.

National HIV testing and diagnosis coverage in sub-Saharan Africa: a new modeling tool for estimating the 'first 90' from program and survey data

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 'Shiny90') 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. Shiny90 provides estimates of HIV testing history, diagnosis rates, and knowledge of HIV status by age and sex. We validate Shiny90 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 Shiny90 can accurately reproduce longitudinal sex-specific trends in HIV testing. Out-of-sample predictions of the fraction of people living with HIV 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 of status 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 model's predictions of knowledge of status are higher than available self-reported HIV awareness estimates, however, suggesting - in line with previous studies - that these self-reports could be affected by nondisclosure of HIV status awareness. Conclusion: Knowledge of HIV status is a key indicator to monitor progress, identify bottlenecks, and target HIV responses. Shiny90 can help countries track progress towards their 'first 90' by leveraging surveys of HIV testing behaviors and annual HTS program data.