Comparison of Empirically Derived and Model-Based Estimates of Key Population HIV Incidence and the Distribution of New Infections by Population Group in Sub-Saharan Africa.

BACKGROUND: The distribution of new HIV infections among key populations, including female sex workers (FSWs), gay men and other men who have sex with men (MSM), and people who inject drugs (PWID) are essential information to guide an HIV response, but data are limited in sub-Saharan Africa (SSA). We analyzed empirically derived and mathematical model-based estimates of HIV incidence among key populations and compared with the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates. METHODS: We estimated HIV incidence among FSW and MSM in SSA by combining meta-analyses of empirical key population HIV incidence relative to the total population incidence with key population size estimates (KPSE) and HIV prevalence. Dynamic HIV transmission model estimates of HIV incidence and percentage of new infections among key populations were extracted from 94 country applications of 9 mathematical models. We compared these with UNAIDS-reported distribution of new infections, implied key population HIV incidence and incidence-to-prevalence ratios. RESULTS: Across SSA, empirical FSW HIV incidence was 8.6-fold (95% confidence interval: 5.7 to 12.9) higher than total population female 15-39 year incidence, and MSM HIV incidence was 41.8-fold (95% confidence interval: 21.9 to 79.6) male 15-29 year incidence. Combined with KPSE, these implied 12% of new HIV infections in 2021 were among FSW and MSM (5% and 7% respectively). In sensitivity analysis varying KPSE proportions within 95% uncertainty range, the proportion of new infections among FSW and MSM was between 9% and 19%. Insufficient data were available to estimate PWID incidence rate ratios. Across 94 models, median proportion of new infections among FSW, MSM, and PWID was 6.4% (interquartile range 3.2%-11.7%), both much lower than the 25% reported by UNAIDS. CONCLUSION: Empirically derived and model-based estimates of HIV incidence confirm dramatically higher HIV risk among key populations in SSA. Estimated proportions of new infections among key populations in 2021 were sensitive to population size assumptions and were substantially lower than estimates reported by UNAIDS.

Measuring HIV Acquisitions Among Partners of Key Populations: Estimates From HIV Transmission Dynamic Models.

BACKGROUND: Key populations (KPs), including female sex workers (FSWs), gay men and other men who have sex with men (MSM), people who inject drugs (PWID), and transgender women (TGW) experience disproportionate risks of HIV acquisition. The UNAIDS Global AIDS 2022 Update reported that one-quarter of all new HIV infections occurred among their non-KP sexual partners. However, this fraction relied on heuristics regarding the ratio of new infections that KPs transmitted to their non-KP partners to the new infections acquired among KPs (herein referred to as "infection ratios"). We recalculated these ratios using dynamic transmission models. SETTING: One hundred seventy-eight settings (106 countries). METHODS: Infection ratios for FSW, MSM, PWID, TGW, and clients of FSW were estimated from 12 models for 2020. RESULTS: Median model estimates of infection ratios were 0.7 (interquartile range: 0.5-1.0; n = 172 estimates) and 1.2 (0.8-1.8; n = 127) for acquisitions from FSW clients and transmissions from FSW to all their non-KP partners, respectively, which were comparable with the previous UNAIDS assumptions (0.2-1.5 across regions). Model estimates for female partners of MSM were 0.5 (0.2-0.8; n = 20) and 0.3 (0.2-0.4; n = 10) for partners of PWID across settings in Eastern and Southern Africa, lower than the corresponding UNAIDS assumptions (0.9 and 0.8, respectively). The few available model estimates for TGW were higher [5.1 (1.2-7.0; n = 8)] than the UNAIDS assumptions (0.1-0.3). Model estimates for non-FSW partners of FSW clients in Western and Central Africa were high (1.7; 1.0-2.3; n = 29). CONCLUSIONS: Ratios of new infections among non-KP partners relative to KP were high, confirming the importance of better addressing prevention and treatment needs among KP as central to reducing overall HIV incidence.

Economic evaluation of improving HIV self-testing among MSM in China using a crowdsourced intervention: a cost-effectiveness analysis.

OBJECTIVES: Crowdsourcing, which taps into the wisdom of crowds, has been successful in generating strategies to enhance HIV self-testing (HIVST) uptake. We determined the cost-effectiveness of a crowdsourced intervention (one-off or annual) compared with a control scenario (no crowdsourcing) among MSM living in China. DESIGN: Economic evaluation. METHODS: We used data from our cluster randomized controlled trial of MSM (NCT02796963). We used a micro-costing approach to measure direct health costs ($USD2017) from a health provider perspective. Using outputs from a dynamic transmission model over a 20-year time horizon, we estimated the incremental cost-effectiveness ratios using cost per disability-adjusted life years (DALYs) averted with 3% discounting. An intervention was considered highly cost-effective if it was less than one gross domestic product (GDP, $8823) per DALY averted. RESULTS: Across all cities, the crowdsourced intervention was highly cost-effective compared with the control scenario (incremental cost-effectiveness ratios ranged from $2263 to 6152 per DALY averted for annual crowdsourcing; $171 to 204 per DALY averted for one-off crowdsourcing). The one-off intervention was cost-saving in Guangzhou and Qingdao. Sensitivity analyses confirmed the robustness of the findings; specifically, changes in discounting, costs of the crowdsourced intervention, costs of HIV testing and cost of antiretroviral therapy did not alter our conclusions. CONCLUSION: Scaling up a one-off or annual crowdsourced HIV prevention intervention in four cities in China was very likely to be cost-effective. Further research is warranted to evaluate the feasibility of scaling up crowdsourced HIV prevention interventions in other settings and populations.

A scoping review of antibiotic use practices and drivers of inappropriate antibiotic use in animal farms in WHO Southeast Asia region.

Antibiotic use (ABU) plays an important role in the proliferation of antimicrobial resistance (AMR). Global antimicrobial consumption in food production is projected to rise by 67% from 2010 to 2030, but available estimates are limited by the scarcity of ABU data and absence of global surveillance systems. The WHO South-East Asia (WHO SEA) region is at high risk of emergence of AMR, likely driven by intensifying farm operations and worsening ABU hotspots. However, little is known about farm-level ABU practices in the region. To summarize emerging evidence and research gaps, we conducted a scoping review of ABU practices following the Arksey and O'Malley methodological framework. We included studies published between 2010 and 2021 on farm-level ABU/AMR in the 11 WHO SEA member states, and databases were last searched on 31 October 2021. Our search strategy identified 184 unique articles, and 25 publications underwent full-text eligibility assessment. Seventeen studies, reported in 18 publications, were included in the scoping review. We found heterogeneity in the categorizations, definitions, and ABU characterization methods used across studies and farm types. Most studies involved poultry, pig, and cattle farms, and only one study examined aquaculture. Most studies evaluated ABU prevalence by asking respondents about the presence or absence of ABU in the farm. Only two studies quantified antibiotic consumption, and sampling bias and lack of standardized data collection methods were identified as key limitations. Emerging evidence that farm workers had difficulty differentiating antibiotics from other substances contributed to the uncertainty about the reliability of self-reported data without other validation techniques. ABU for growth promotion and treatment were prevalent. We found a large overlap in the critically important antibiotics used in farm animals and humans. The ease of access to antibiotics compounded by the difficulties in accessing quality veterinary care and preventive services likely drive inappropriate ABU in complex ways.

Modelling the effect of COVID-19 mass vaccination on acute hospital admissions.

BACKGROUND: Managing high levels of acute COVID-19 bed occupancy can affect the quality of care provided to both affected patients and those requiring other hospital services. Mass vaccination has offered a route to reduce societal restrictions while protecting hospitals from being overwhelmed. Yet, early in the mass vaccination effort, the possible impact on future bed pressures remained subject to considerable uncertainty. OBJECTIVE: The aim of this study was to model the effect of vaccination on projections of acute and intensive care bed demand within a 1 million resident healthcare system located in South West England. METHODS: An age-structured epidemiological model of the susceptible-exposed-infectious-recovered type was fitted to local data up to the time of the study, in early March 2021. Model parameters and vaccination scenarios were calibrated through a system-wide multidisciplinary working group, comprising public health intelligence specialists, healthcare planners, epidemiologists and academics. Scenarios assumed incremental relaxations to societal restrictions according to the envisaged UK Government timeline, with all restrictions to be removed by 21 June 2021. RESULTS: Achieving 95% vaccine uptake in adults by 31 July 2021 would not avert the third wave in autumn 2021 but would produce a median peak bed requirement ∼6% (IQR: 1-24%) of that experienced during the second wave (January 2021). A 2-month delay in vaccine rollout would lead to significantly higher peak bed occupancy, at 66% (11-146%) of that of the second wave. If only 75% uptake was achieved (the amount typically associated with vaccination campaigns), then the second wave peak for acute and intensive care beds would be exceeded by 4% and 19%, respectively, an amount which would seriously pressure hospital capacity. CONCLUSION: Modelling influenced decision-making among senior managers in setting COVID-19 bed capacity levels, as well as highlighting the importance of public health in promoting high vaccine uptake among the population. Forecast accuracy has since been supported by actual data collected following the analysis, with observed peak bed occupancy falling comfortably within the inter-quartile range of modelled projections.

The impact of disruptions due to COVID-19 on HIV transmission and control among men who have sex with men in China.

INTRODUCTION: The COVID-19 pandemic is impacting HIV care globally, with gaps in HIV treatment expected to increase HIV transmission and HIV-related mortality. We estimated how COVID-19-related disruptions could impact HIV transmission and mortality among men who have sex with men (MSM) in four cities in China, over a one- and five-year time horizon. METHODS: Regional data from China indicated that the number of MSM undergoing facility-based HIV testing reduced by 59% during the COVID-19 pandemic, alongside reductions in ART initiation (34%), numbers of all sexual partners (62%) and consistency of condom use (25%), but initial data indicated no change in viral suppression. A mathematical model of HIV transmission/treatment among MSM was used to estimate the impact of disruptions on HIV infections/HIV-related deaths. Disruption scenarios were assessed for their individual and combined impact over one and five years for 3/4/6-month disruption periods, starting from 1 January 2020. RESULTS: Our model predicted new HIV infections and HIV-related deaths would be increased most by disruptions to viral suppression, with 25% reductions (25% virally suppressed MSM stop taking ART) for a three-month period increasing HIV infections by 5% to 14% over one year and deaths by 7% to 12%. Observed reductions in condom use increased HIV infections by 5% to 14% but had minimal impact (<1%) on deaths. Smaller impacts on infections and deaths (<3%) were seen for disruptions to facility HIV testing and ART initiation, but reduced partner numbers resulted in 11% to 23% fewer infections and 0.4% to 1.0% fewer deaths. Longer disruption periods (4/6 months) amplified the impact of disruption scenarios. When realistic disruptions were modelled simultaneously, an overall decrease in new HIV infections occurred over one year (3% to 17%), but not for five years (1% increase to 4% decrease), whereas deaths mostly increased over one year (1% to 2%) and five years (1.2 increase to 0.3 decrease). CONCLUSIONS: The overall impact of COVID-19 on new HIV infections and HIV-related deaths is dependent on the nature, scale and length of the various disruptions. Resources should be directed to ensuring levels of viral suppression and condom use are maintained to mitigate any adverse effects of COVID-19-related disruption on HIV transmission and control among MSM in China.

One Health drivers of antibacterial resistance: Quantifying the relative impacts of human, animal and environmental use and transmission.

OBJECTIVES: Antibacterial resistance (ABR) is a major global health security threat, with a disproportionate burden on lower-and middle-income countries (LMICs). It is not understood how 'One Health', where human health is co-dependent on animal health and the environment, might impact the burden of ABR in LMICs. Thailand's 2017 "National Strategic Plan on Antimicrobial Resistance" (NSP-AMR) aims to reduce AMR morbidity by 50% through 20% reductions in human and 30% in animal antibacterial use (ABU). There is a need to understand the implications of such a plan within a One Health perspective. METHODS: A model of ABU, gut colonisation with extended-spectrum beta-lactamase (ESBL)-producing bacteria and transmission was calibrated using estimates of the prevalence of ESBL-producing bacteria in Thailand. This model was used to project the reduction in human ABR over 20 years (2020-2040) for each One Health driver, including individual transmission rates between humans, animals and the environment, and to estimate the long-term impact of the NSP-AMR intervention. RESULTS: The model predicts that human ABU was the most important factor in reducing the colonisation of humans with resistant bacteria (maximum 65.7-99.7% reduction). The NSP-AMR is projected to reduce human colonisation by 6.0-18.8%, with more ambitious targets (30% reductions in human ABU) increasing this to 8.5-24.9%. CONCLUSIONS: Our model provides a simple framework to explain the mechanisms underpinning ABR, suggesting that future interventions targeting the simultaneous reduction of transmission and ABU would help to control ABR more effectively in Thailand.

The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study.

BACKGROUND: During the COVID-19 pandemic, many intensive care units have been overwhelmed by unprecedented levels of demand. Notwithstanding ethical considerations, the prioritization of patients with better prognoses may support a more effective use of available capacity in maximizing aggregate outcomes. This has prompted various proposed triage criteria, although in none of these has an objective assessment been made in terms of impact on number of lives and life-years saved. DESIGN: An open-source computer simulation model was constructed for approximating the intensive care admission and discharge dynamics under triage. The model was calibrated from observational data for 9505 patient admissions to UK intensive care units. To explore triage efficacy under various conditions, scenario analysis was performed using a range of demand trajectories corresponding to differing nonpharmaceutical interventions. RESULTS: Triaging patients at the point of expressed demand had negligible effect on deaths but reduces life-years lost by up to 8.4% (95% confidence interval: 2.6% to 18.7%). Greater value may be possible through "reverse triage", that is, promptly discharging any patient not meeting the criteria if admission cannot otherwise be guaranteed for one who does. Under such policy, life-years lost can be reduced by 11.7% (2.8% to 25.8%), which represents 23.0% (5.4% to 50.1%) of what is operationally feasible with no limit on capacity and in the absence of improved clinical treatments. CONCLUSIONS: The effect of simple triage is limited by a tradeoff between reduced deaths within intensive care (due to improved outcomes) and increased deaths resulting from declined admission (due to lower throughput given the longer lengths of stay of survivors). Improvements can be found through reverse triage, at the expense of potentially complex ethical considerations.

Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: a mathematical modelling framework.

OBJECTIVES: To develop a regional model of COVID-19 dynamics for use in estimating the number of infections, deaths and required acute and intensive care (IC) beds using the South West England (SW) as an example case. DESIGN: Open-source age-structured variant of a susceptible-exposed-infectious-recovered compartmental mathematical model. Latin hypercube sampling and maximum likelihood estimation were used to calibrate to cumulative cases and cumulative deaths. SETTING: SW at a time considered early in the pandemic, where National Health Service authorities required evidence to guide localised planning and support decision-making. PARTICIPANTS: Publicly available data on patients with COVID-19. PRIMARY AND SECONDARY OUTCOME MEASURES: The expected numbers of infected cases, deaths due to COVID-19 infection, patient occupancy of acute and IC beds and the reproduction ('R') number over time. RESULTS: SW model projections indicate that, as of 11 May 2020 (when 'lockdown' measures were eased), 5793 (95% credible interval (CrI) 2003 to 12 051) individuals were still infectious (0.10% of the total SW population, 95% CrI 0.04% to 0.22%), and a total of 189 048 (95% CrI 141 580 to 277 955) had been infected with the virus (either asymptomatically or symptomatically), but recovered, which is 3.4% (95% CrI 2.5% to 5.0%) of the SW population. The total number of patients in acute and IC beds in the SW on 11 May 2020 was predicted to be 701 (95% CrI 169 to 1543) and 110 (95% CrI 8 to 464), respectively. The R value in SW was predicted to be 2.6 (95% CrI 2.0 to 3.2) prior to any interventions, with social distancing reducing this to 2.3 (95% CrI 1.8 to 2.9) and lockdown/school closures further reducing the R value to 0.6 (95% CrI 0.5 to 0.7). CONCLUSIONS: The developed model has proved a valuable asset for regional healthcare services. The model will be used further in the SW as the pandemic evolves, and-as open-source software-is portable to healthcare systems in other geographies.

Modelling the impact of an HIV testing intervention on HIV transmission among men who have sex with men in China.

OBJECTIVES: An intervention developed through participatory crowdsourcing methods increased HIV self-testing among men who have sex with men [MSM; relative risk (RR) = 1.89]. We estimated the long-term impact of this intervention on HIV transmission among MSM in four cities (Guangzhou, Shenzhen, Jinan and Qingdao). METHODS: A mathematical model of HIV transmission, testing and treatment among MSM in China was parameterized using city-level demographic and sexual behaviour data and calibrated to HIV prevalence, diagnosis and antiretroviral therapy (ART) coverage data. The model was used to project the HIV infections averted over 20 years (2016-2036) from the intervention to increase self-testing, compared with current testing rates. RESULTS: Running the intervention once would avert < 2.2% infections over 20 years. Repeating the intervention (RR = 1.89) annually would avert 6.4-10.7% of new infections, while further increases in the self-testing rate (hypothetical RR = 3) would avert 11.7-20.7% of new infections. CONCLUSIONS: Repeated annual interventions would give a three- to seven-fold increase in long-term impact compared with a one-off intervention. Other interventions will be needed to more effectively reduce the HIV burden in this population.

Estimating the impact of disruptions due to COVID-19 on HIV transmission and control among men who have sex with men in China.

Introduction The COVID-19 pandemic is impacting HIV care globally, with gaps in HIV treatment expected to increase HIV transmission and HIV-related mortality. We estimated how COVID-19-related disruptions could impact HIV transmission and mortality among men who have sex with men (MSM) in four cities in China. Methods Regional data from China indicated that the number of MSM undergoing facility-based HIV testing reduced by 59% during the COVID-19 pandemic, alongside reductions in ART initiation (34%), numbers of sexual partners (62%) and consistency of condom use (25%). A deterministic mathematical model of HIV transmission and treatment among MSM in China was used to estimate the impact of these disruptions on the number of new HIV infections and HIV-related deaths. Disruption scenarios were assessed for their individual and combined impact over 1 and 5 years for a 3-, 4- or 6-month disruption period. Results Our China model predicted that new HIV infections and HIV-related deaths would be increased most by disruptions to viral suppression, with 25% reductions for a 3-month period increasing HIV infections by 5-14% over 1 year and deaths by 7-12%. Observed reductions in condom use increased HIV infections by 5-14% but had minimal impact (<1%) on deaths. Smaller impacts on infections and deaths (<3%) were seen for disruptions to facility testing and ART initiation, but reduced partner numbers resulted in 11-23% fewer infections and 0.4-1.0% fewer deaths. Longer disruption periods of 4 and 6 months amplified the impact of combined disruption scenarios. When all realistic disruptions were modelled simultaneously, an overall decrease in new HIV infections was always predicted over one year (3-17%), but not over 5 years (1% increase - 4% decrease), while deaths mostly increased over one year (1-2%) and 5 years (1.2 increase - 0.3 decrease). Conclusions The overall impact of COVID-19 on new HIV infections and HIV-related deaths is dependent on the nature, scale and length of the various disruptions. Resources should be directed to ensuring levels of viral suppression and condom use are maintained to mitigate any adverse effects of COVID-19 related disruption on HIV transmission and control among MSM in China.

Interactions between immunotoxicants and parasite stress: Implications for host health.

Many organisms face a wide variety of biotic and abiotic stressors which reduce individual survival, interacting to further reduce fitness. Here we studied the effects of two such interacting stressors: immunotoxicant exposure and parasite infection. We model the dynamics of a within-host infection and the associated immune response of an individual. We consider both the indirect sub-lethal effects on immunosuppression and the direct effects on health and mortality of individuals exposed to toxicants. We demonstrate that sub-lethal exposure to toxicants can promote infection through the suppression of the immune system. This happens through the depletion of the immune response which causes rapid proliferation in parasite load. We predict that the within-host parasite density is maximised by an intermediate toxicant exposure, rather than continuing to increase with toxicant exposure. In addition, high toxicant exposure can alter cellular regulation and cause the breakdown of normal healthy tissue, from which we infer higher mortality risk of the host. We classify this breakdown into three phases of increasing toxicant stress, and demonstrate the range of conditions under which toxicant exposure causes failure at the within-host level. These phases are determined by the relationship between the immunity status, overall cellular health and the level of toxicant exposure. We discuss the implications of our model in the context of individual bee health. Our model provides an assessment of how pesticide stress and infection interact to cause the breakdown of the within-host dynamics of individual bees.

Stress-mediated Allee effects can cause the sudden collapse of honey bee colonies.

The recent rapid decline in global honey bee populations could have significant implications for ecological systems, economics and food security. No single cause of honey bee collapse has yet to be identified, although pesticides, mites and other pathogens have all been shown to have a sublethal effect. We present a model of a functioning bee hive and introduce external stress to investigate the impact on the regulatory processes of recruitment to the forager class, social inhibition and the laying rate of the queen. The model predicts that constant density-dependent stress acting through an Allee effect on the hive can result in sudden catastrophic switches in dynamical behaviour and the eventual collapse of the hive. The model proposes that around a critical point the hive undergoes a saddle-node bifurcation, and that a small increase in model parameters can have irreversible consequences for the entire hive. We predict that increased stress levels can be counteracted by a higher laying rate of the queen, lower levels of forager recruitment or lower levels of natural mortality of foragers, and that increasing social inhibition can not maintain the colony under high levels of stress. We lay the theoretical foundation for sudden honey bee collapse in order to facilitate further experimental and theoretical consideration.