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Article ; Online: Examining changes in sexual lifestyles in Britain between 1990-2010: a latent class analysis approach.

Muschialli, Luke / Samartsidis, Pantelis / Presanis, Anne M / Mercer, Catherine H

2024 Volume 24, Issue 1, Page(s) 366

Abstract: Background: Understanding sexual lifestyles and how they change over time is important for determining the likelihood of sexual health outcomes. Standard descriptive and regression methods are limited in their ability to capture multidimensional ... ...

Abstract	Background: Understanding sexual lifestyles and how they change over time is important for determining the likelihood of sexual health outcomes. Standard descriptive and regression methods are limited in their ability to capture multidimensional concepts such as sexual lifestyles. Latent Class Analysis (LCA) is a mixture modelling method that generates a categorical latent variable to derive homogenous groups from a heterogeneous population. Our study investigates (1) the potential of LCA to assess change over time in sexual lifestyles and (2) how quantifying this change using LCA compares to previous findings using standard approaches. Methods: Probability-sampled data from three rounds of the National Survey of Sexual Attitudes and Lifestyle (Natsal) were used, restricted to sexually active participants (i.e., those reporting sexual partners in the past year) aged 16-44 years (N Results: We successfully used a LCA approach to examine change in sexual lifestyle over time. We observed a statistically significant increase between 1990 and 2010 in the proportion of men (χ Conclusion: Our results indicate the viability of LCA models to assess change over time for complex behavioural phenomena. They align with previous findings, namely changing sexual lifestyles in Britain in recent decades, partnership number driving class assignment, and significant sex differences in sexual lifestyles. This approach can be used to extend previous LCA models (e.g., to investigate the impact of COVID-19 on sexual lifestyles) and to support empirical evidence of change over time, facilitating more nuanced public health policy.
MeSH term(s)	Female ; Humans ; Male ; Latent Class Analysis ; United Kingdom/epidemiology ; Health Surveys ; Sexual Behavior ; Sexual Partners ; Life Style ; HIV Infections ; Sexually Transmitted Diseases/epidemiology
Language	English
Publishing date	2024-02-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2041338-5
ISSN	1471-2458 ; 1471-2458
ISSN (online)	1471-2458
ISSN	1471-2458
DOI	10.1186/s12889-024-17850-1
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Article ; Online: Dynamic predictions from longitudinal CD4 count measures and time to death of HIV/AIDS patients using a Bayesian joint model.

Muhammed, Feysal Kemal / Belay, Denekew Bitew / Presanis, Anne M / Sebu, Aboma Temesgen

Scientific African

2023 Volume 19, Page(s) e01519

Abstract: A Bayesian joint modeling approach to dynamic prediction of HIV progression and mortality allows individualized predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific ... ...

Abstract	A Bayesian joint modeling approach to dynamic prediction of HIV progression and mortality allows individualized predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival. Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging. The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7-39 months. The estimates of the association structure parameters from two of the three models considered indicated that the HIV mortality hazard at any time point is associated with the rate of change in the underlying value of the CD4 count. Model averaging the dynamic predictions resulted in only one of the hypothesized association structures having non-zero weight in almost all time points for each individual, with the exception of twelve patients, for whom other association structures were preferred at a few time points. The predictions were found to be different when we averaged them over models than when we derived them from the highest posterior weight model alone. The model with highest posterior weight for almost all time points for each individual gave an estimate of the association parameter of -0.02 implying that for a unit increase in the CD4 count, the hazard of HIV mortality decreases by a factor (hazard ratio) of 0.98. Functional status and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements.
Language	English
Publishing date	2023-01-02
Publishing country	Netherlands
Document type	Journal Article
ISSN	2468-2276
ISSN (online)	2468-2276
DOI	10.1016/j.sciaf.2022.e01519
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Article ; Online: Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals.

Heath, Bethany / Evans, Stephanie / Robertson, David S / Robotham, Julie V / Villar, Sofía S / Presanis, Anne M

BMC infectious diseases

2023 Volume 23, Issue 1, Page(s) 900

Abstract: Background: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) ... ...

Abstract	Background: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing). Methods: Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers. Results: Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests. Conclusions: Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.
MeSH term(s)	Humans ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19/prevention & control ; Retrospective Studies ; Hospitals ; Health Personnel ; Cross Infection/diagnosis ; Cross Infection/epidemiology ; Cross Infection/prevention & control
Language	English
Publishing date	2023-12-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2041550-3
ISSN	1471-2334 ; 1471-2334
ISSN (online)	1471-2334
ISSN	1471-2334
DOI	10.1186/s12879-023-08881-x
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Article: Evidence Synthesis for Stochastic Epidemic Models.

Birrell, Paul J / De Angelis, Daniela / Presanis, Anne M

Statistical science : a review journal of the Institute of Mathematical Statistics

2020 Volume 33, Issue 1, Page(s) 34–43

Abstract: In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This ... ...

Abstract	In recent years, the role of epidemic models in informing public health policies has progressively grown. Models have become increasingly realistic and more complex, requiring the use of multiple data sources to estimate all quantities of interest. This review summarises the different types of stochastic epidemic models that use evidence synthesis and highlights current challenges.
Language	English
Publishing date	2020-02-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	2009740-2
ISSN	2168-8745 ; 0883-4237
ISSN (online)	2168-8745
ISSN	0883-4237
DOI	10.1214/17-STS631
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Article ; Online: Estimation of the impact of hospital-onset SARS-CoV-2 infections on length of stay in English hospitals using causal inference.

Stimson, James / Pouwels, Koen B / Hope, Russell / Cooper, Ben S / Presanis, Anne M / Robotham, Julie V

BMC infectious diseases

2022 Volume 22, Issue 1, Page(s) 922

Abstract: Background: From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, ...

Abstract	Background: From March 2020 through August 2021, 97,762 hospital-onset SARS-CoV-2 infections were detected in English hospitals. Resulting excess length of stay (LoS) created a potentially substantial health and economic burden for patients and the NHS, but we are currently unaware of any published studies estimating this excess. Methods: We implemented appropriate causal inference methods to determine the extent to which observed additional hospital stay is attributable to the infection rather than the characteristics of the patients. Hospital admissions records were linked to SARS-CoV-2 test data to establish the study population (7.5 million) of all non-COVID-19 admissions to English hospitals from 1st March 2020 to 31st August 2021 with a stay of at least two days. The excess LoS due to hospital-onset SARS-CoV-2 infection was estimated as the difference between the mean LoS observed and in the counterfactual where infections do not occur. We used inverse probability weighted Kaplan-Meier curves to estimate the mean survival time if all hospital-onset SARS-CoV-2 infections were to be prevented, the weights being based on the daily probability of acquiring an infection. The analysis was carried out for four time periods, reflecting phases of the pandemic differing with respect to overall case numbers, testing policies, vaccine rollout and prevalence of variants. Results: The observed mean LoS of hospital-onset cases was higher than for non-COVID-19 hospital patients by 16, 20, 13 and 19 days over the four phases, respectively. However, when the causal inference approach was used to appropriately adjust for time to infection and confounding, the estimated mean excess LoS caused by hospital-onset SARS-CoV-2 was: 2.0 [95% confidence interval 1.8-2.2] days (Mar-Jun 2020), 1.4 [1.2-1.6] days (Sep-Dec 2020); 0.9 [0.7-1.1] days (Jan-Apr 2021); 1.5 [1.1-1.9] days (May-Aug 2021). Conclusions: Hospital-onset SARS-CoV-2 is associated with a small but notable excess LoS, equivalent to 130,000 bed days. The comparatively high LoS observed for hospital-onset COVID-19 patients is mostly explained by the timing of their infections relative to admission. Failing to account for confounding and time to infection leads to overestimates of additional length of stay and therefore overestimates costs of infections, leading to inaccurate evaluations of control strategies.
Language	English
Publishing date	2022-12-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2041550-3
ISSN	1471-2334 ; 1471-2334
ISSN (online)	1471-2334
ISSN	1471-2334
DOI	10.1186/s12879-022-07870-w
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Book ; Online: Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal Methods

Corbella, Alice / Presanis, Anne M / Birrell, Paul J / De Angelis, Daniela

2022

Abstract: Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and severity dynamics. ...

Abstract	Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from the unobserved epidemic process including transmission and severity dynamics. This paper explores important challenges to the use of state-space models for epidemic inference when multiple dependent datasets are analysed. We propose a new semi-stochastic model that exploits deterministic approximations for large-scale transmission dynamics while retaining stochasticity in the occurrence and reporting of relatively rare severe events. This model is suitable for many real-time situations including large seasonal epidemics and pandemics. Within this context, we develop algorithms to provide exact parameter inference and test them via simulation. Finally, we apply our joint model and the proposed algorithm to several surveillance data on the 2017-18 influenza epidemic in England to reconstruct transmission dynamics and estimate the daily new influenza infections as well as severity indicators as the case-hospitalisation risk and the hospital-intensive care risk. Comment: 22 pages, 7 figures, 4 tables, 1 algorithm. Submitted to the Annals of Applied Statistics
Keywords	Statistics - Applications
Subject code	612
Publishing date	2022-04-19
Publishing country	us
Document type	Book ; Online
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Article ; Online: Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, a cohort study.

Kirwan, Peter D / Charlett, Andre / Birrell, Paul / Elgohari, Suzanne / Hope, Russell / Mandal, Sema / De Angelis, Daniela / Presanis, Anne M

Nature communications

2022 Volume 13, Issue 1, Page(s) 4834

Abstract: Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 ... ...

Abstract	Widespread vaccination campaigns have changed the landscape for COVID-19, vastly altering symptoms and reducing morbidity and mortality. We estimate trends in mortality by month of admission and vaccination status among those hospitalised with COVID-19 in England between March 2020 to September 2021, controlling for demographic factors and hospital load. Among 259,727 hospitalised COVID-19 cases, 51,948 (20.0%) experienced mortality in hospital. Hospitalised fatality risk ranged from 40.3% (95% confidence interval 39.4-41.3%) in March 2020 to 8.1% (7.2-9.0%) in June 2021. Older individuals and those with multiple co-morbidities were more likely to die or else experienced longer stays prior to discharge. Compared to unvaccinated people, the hazard of hospitalised mortality was 0.71 (0.67-0.77) with a first vaccine dose, and 0.56 (0.52-0.61) with a second vaccine dose. Compared to hospital load at 0-20% of the busiest week, the hazard of hospitalised mortality during periods of peak load (90-100%), was 1.23 (1.12-1.34). The prognosis for people hospitalised with COVID-19 in England has varied substantially throughout the pandemic and according to case-mix, vaccination, and hospital load. Our estimates provide an indication for demands on hospital resources, and the relationship between hospital burden and outcomes.
MeSH term(s)	COVID-19/epidemiology ; COVID-19/prevention & control ; Cohort Studies ; Hospitals ; Humans ; SARS-CoV-2 ; Vaccines
Chemical Substances	Vaccines
Language	English
Publishing date	2022-08-17
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-32458-y
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Article ; Online: Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic.

Seaman, Shaun R / Nyberg, Tommy / Overton, Christopher E / Pascall, David J / Presanis, Anne M / De Angelis, Daniela

Statistical methods in medical research

2022 Volume 31, Issue 10, Page(s) 1942–1958

Abstract: When comparing the risk of a post-infection binary outcome, for example, hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection. Typically, the infection time is unknown and positive test ... ...

Abstract	When comparing the risk of a post-infection binary outcome, for example, hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection. Typically, the infection time is unknown and positive test time used as a proxy for it. Positive test time may also be used when assessing how risk of the outcome changes over calendar time. We show that if time from infection to positive test is correlated with the outcome, the risk conditional on positive test time is a function of the trajectory of infection incidence. Hence, a risk ratio adjusted for positive test time can be quite different from the risk ratio adjusted for infection time. We propose a simple sensitivity analysis that indicates how risk ratios adjusted for positive test time and infection time may differ. This involves adjusting for a shifted positive test time, shifted to make the difference between it and infection time uncorrelated with the outcome. We illustrate this method by reanalysing published results on the relative risk of hospitalisation following infection with the Alpha versus pre-existing variants of SARS-CoV-2. Results indicate the relative risk adjusted for infection time may be lower than that adjusted for positive test time.
MeSH term(s)	COVID-19/epidemiology ; Epidemics ; Humans ; SARS-CoV-2
Language	English
Publishing date	2022-06-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221107105
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Article ; Online: A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19.

Jackson, Christopher H / Tom, Brian Dm / Kirwan, Peter D / Mandal, Sema / Seaman, Shaun R / Kunzmann, Kevin / Presanis, Anne M / De Angelis, Daniela

Statistical methods in medical research

2022 Volume 31, Issue 9, Page(s) 1656–1674

Abstract: We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate ...

Abstract	We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
MeSH term(s)	COVID-19 ; Hospitalization ; Hospitals ; Humans ; Intensive Care Units ; Probability
Language	English
Publishing date	2022-07-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1136948-6
ISSN	1477-0334 ; 0962-2802
ISSN (online)	1477-0334
ISSN	0962-2802
DOI	10.1177/09622802221106720
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Article ; Online: Misclassification bias in estimating clinical severity of SARS-CoV-2 variants - Authors' reply.

Nyberg, Tommy / Ferguson, Neil M / Blake, Joshua / Hinsley, Wes / Bhatt, Samir / De Angelis, Daniela / Thelwall, Simon / Presanis, Anne M

Lancet (London, England)

2022 Volume 400, Issue 10355, Page(s) 809–810

MeSH term(s)	COVID-19 ; Humans ; SARS-CoV-2/genetics
Language	English
Publishing date	2022-09-07
Publishing country	England
Document type	Letter ; Comment
ZDB-ID	3306-6
ISSN	1474-547X ; 0023-7507 ; 0140-6736
ISSN (online)	1474-547X
ISSN	0023-7507 ; 0140-6736
DOI	10.1016/S0140-6736(22)01432-5
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