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Journal articleTurner H, Rivillas-Garcia JC, Prinja S, et al., 2025,
An introduction to costing and the types of costs used within health economic studies
, PharmacoEconomics - Open, ISSN: 2509-4254The number of published health economic analyses, especially economic evaluations, has rapidly expanded globally since the 1990s, and costs are an essential component of such studies. Cost is a general term that refers to the value of the resources/inputs used to produce a good or service. However, within health economics, there are several different types of costs (such as financial, economic, unit, average, etc). The terminology and application of these cost types often differ, leading to inconsistencies in the health economics literature. These inconsistencies create challenges in comparing studies and hinder the use of health economic analyses to effectively inform policy decisions. This paper aims to provide an up-to-date overview of the cost types, key cost terms, and definitions of different cost measures used within health economics, while highlighting key inconsistencies in the literature. We also discuss common adjustments made to cost data, such as accounting for inflation, discounting, and currency conversions, as well as the influence of economies of scale and scope on cost estimates. We highlight the different definitions/categories for the different types of costs are not mutually exclusive and that the type of cost that should be used will depend on the purpose of the study, highlighting recommendations of what to do in practice where relevant. The content was tailored to be relevant across both high-income and low and middle-income (LMIC) country contexts.
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Journal articleWilliams LR, Voysey M, Pollard AJ, et al., 2025,
A novel approach for estimating vaccine efficacy for infections with multiple outcomes: application to a COVID-19 vaccine trial
, AJE Advances: Research in Epidemiology, Vol: 1<jats:title>Abstract</jats:title> <jats:p>Vaccines can provide protection against infection or limit disease severity. Vaccine efficacy (VE) is typically evaluated independently for different outcomes, but this does not provide insight into the mechanism of the protective effect and can cause biased estimates of VE. We propose a new conceptual framework and statistical implementation for VE estimation for infections with multiple possible outcomes of infection: joint analysis of multiple outcomes in vaccine efficacy trials (JAMOVET). JAMOVET is a Bayesian hierarchical regression model that controls for biases and can evaluate covariates for VE, the hazard of infection, and the probability of progression. We applied JAMOVET to simulated data, and data from COV002 (NCT04400838), a phase 2/3 trial of ChAdOx1 nCoV-19 (AZD1222) vaccine. Simulations showed that biases are corrected by explicitly modeling disease progression and imperfect test characteristics. JAMOVET estimated ChAdOx1 nCoV-19 VE against infection (${\mathrm{VE}}_{in}$) at 55% (95% credible interval [CrI] 35-70) and progression to symptoms (${\mathrm{VE}}_{pr}$) at 44% (95% CrI 26-59). This implies a VE against symptomatic infection of 75% (95% CrI 62-85), consistent with published trial estimates. JAMOVET is a powerful tool for evaluating diseases with multiple dependent outcomes and can be used to adjust for biases and identify predictors of key outcomes.</jats:p>
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Journal articleAgostinho A, Chalot E, Teixeira D, et al., 2025,
Semi-automated surveillance of surgical site infections using machine learning and rule-based classification models
, NPJ DIGITAL MEDICINE, Vol: 8, ISSN: 2398-6352 -
Journal articleWehrli S, Hartner A-M, Boender TS, et al., 2025,
Information Pathways and Voids in Critical German Online Communities During the COVID-19 Vaccination Discourse: Cross-Platform and Mixed Methods Analysis
, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 27, ISSN: 1439-4456 -
Journal articleHancock P, Hui T-Y, Epopa PS, et al., 2025,
Requirements for designing cluster randomised control trials to detect suppression of malaria vector population densities
, BMC Biology, Vol: 23, ISSN: 1741-7007BackgroundNovel interventions for mosquito-borne disease control which release modified mosquitoes that are sterilised or genetically modified to cause offspring inviability are progressing towards field applications. Cluster randomised control trials (CRCTs) could provide robust assessment of intervention efficacy in suppressing mosquito populations in field environments, but guidance on designing CRCTs to detect mosquito suppression impacts is limited.ResultsWe developed statistical models to simulate CRCTs, informed by a 5-year time series measuring densities of malaria vector species from the Anopheles gambiae complex in four villages in western Burkina Faso. We estimated requirements for parallel and step wedge designs, varying the targeted vector species, the suppression effect and the monitoring regime. For a suppression effect of 50%, 21–22 clusters were required to detect suppression with 90% power when all An. gambiae complex species were targeted, while 24–26 clusters were required when only An. coluzzii was targeted and 60–66 clusters were required when only An. gambiae was targeted. For stronger suppression effects, required trial sizes depended less on target species, with 9–10 clusters being sufficient to detect a 90% suppression effect. We investigated how reducing sampling effort, by sampling fewer houses and restricting sampling to rainy season months, affected statistical power.ConclusionsOur results provide empirically based guidance for designing CRCTs to evaluate interventions aiming to suppress malaria vector populations.
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Journal articleScheidwasser N, Poulsen LL, Leow PR, et al., 2025,
Deep learning from videography as a tool for measuring <i>E. coli</i> infection in poultry
, ROYAL SOCIETY OPEN SCIENCE, Vol: 12, ISSN: 2054-5703 -
Journal articleDi Lauro F, Probert WJM, Pickles M, et al., 2025,
Large connected components in sexual networks and their role in HIV transmission in Sub-Saharan Africa: A model-based analysis of HPTN 071 (PopART) data
, JOURNAL OF THEORETICAL BIOLOGY, Vol: 613, ISSN: 0022-5193 -
Journal articleSteyn N, Chadeau M, Elliott P, et al., 2025,
A Bayesian model for repeated cross-sectional epidemic prevalence survey data
, PLoS Computational Biology, ISSN: 1553-734XEpidemic prevalence surveys monitor the spread of an infectious disease by regularly testing representative samples of a population for infection. State-of-the-art Bayesian approaches for analysing epidemic survey data were constructed independently and under pressure during the COVID-19 pandemic. In this paper, we compare two existing approaches (one leveraging Bayesian P-splines and the other approximate Gaussianprocesses) with a novel approach (leveraging a random walk and fit using sequential Monte Carlo) for smoothing and performing inference on epidemic survey data. We use our simpler approach to investigate the impact of survey design and underlying epidemic dynamics on the quality of estimates. We then incorporate theseconsiderations into the existing approaches and compare all three on simulated data and on real-world data from the SARS-CoV-2 REACT-1 prevalence study in England. All three approaches, once appropriate considerations are made, produce similar estimatesof infection prevalence; however, estimates of the growth rate and instantaneous reproduction number are more sensitive to underlying assumptions. Interactivenotebooks applying all three approaches are also provided alongside recommendations on hyperparameter selection and other practical guidance, with some cases resulting in orders-of-magnitude faster runtime.
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Journal articleYamey G, McDade KK, Anderson RM, et al., 2025,
Vaccine value profile for schistosomiasis
, VACCINE, Vol: 64, ISSN: 0264-410X- Cite
- Citations: 3
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Journal articleStone J, Mutai KK, Artenie A, et al., 2025,
The impact of cuts in the US President's Emergency Plan for AIDS Relief funding for HIV pre-exposure prophylaxis in sub-Saharan Africa: a modelling study
, The Lancet HIV, Vol: 12, Pages: e712-e721, ISSN: 2352-3018BackgroundIn January, 2025, the US Government issued a directive, pausing all foreign aid programmes. This directive included a 90-day pausing of all US President's Emergency Plan for AIDS Relief (PEPFAR) funding for HIV oral pre-exposure prophylaxis (PrEP) except for pregnant and breastfeeding women, with a return to funding for PrEP looking increasingly unlikely. We aimed to estimate the impact of a funding pause for PrEP on HIV infections in sub-Saharan Africa.MethodsIn this mathematical modelling study, we developed a static HIV transmission model incorporating PrEP, parameterised with estimates of population size, HIV prevalence and incidence, and PrEP effectiveness for different subpopulations (including key populations) in each PEPFAR-funded sub-Saharan African country. Key populations were men who have sex with men, female sex workers, transgender women, and people who inject drugs. We used PEPFAR reporting on numbers of people in different subpopulations returning for oral PrEP for each country in July to September, 2024, as the estimated number using oral PrEP provided by PEPFAR. For each country and subpopulation, we modelled the relative and absolute increase in new primary HIV infections resulting from removing this funded PrEP for a year and the number of secondary infections that could result from these primary infections during the next 5 years.FindingsTowards the end of 2024, 719 384 individuals who were not breastfeeding or pregnant, including 205 868 people from key populations, received PEPFAR-funded PrEP across 28 sub-Saharan African countries. The estimated proportion of HIV-negative key population individuals receiving PEPFAR-funded PrEP (ie, the coverage) ranged from 2·6% (95% uncertainty interval 2·0–3·4) in people who inject drugs to 5·0% (4·5–5·9) in female sex workers. Estimated coverage among non-key population men was less than 0·1% (<0·1 to <0·1) and in wo
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