Prevalence of multiple long-term conditions (multimorbidity) in England: a whole population study of over 60 million people.

OBJECTIVES: To determine the prevalence of multiple long-term conditions (MLTC) at whole English population level, stratifying by age, sex, socioeconomic status and ethnicity. DESIGN: A whole population study. SETTING: Individuals registered with a general practice in England and alive on 31 March 2020. PARTICIPANTS: 60,004,883 individuals. MAIN OUTCOME MEASURES: MLTC prevalence, defined as two or more of 35 conditions derived from a number of national patient-level datasets. Multivariable logistic regression was used to assess the independent associations of age, sex, ethnicity and deprivation decile with odds of MLTC. RESULTS: The overall prevalence of MLTC was 14.8% (8,878,231), varying from 0.9% (125,159) in those aged 0-19 years to 68.2% (1,905,979) in those aged 80 years and over. In multivariable regression analyses, compared with the 50-59 reference group, the odds ratio was 0.04 (95% confidence interval (CI): 0.04-0.04; p < 0.001) for those aged 0-19 years and 10.21 (10.18-10.24; p < 0.001) for those aged 80 years and over. Odds were higher for men compared with women, 1.02 (1.02-1.02; p < 0.001), for the most deprived decile compared with the least deprived, 2.26 (2.25-2.27; p < 0.001), and for Asian ethnicity compared with those of white ethnicity, 1.05 (1.04-1.05; p < 0.001). Odds were lower for black, mixed and other ethnicities (0.94 (0.94-0.95) p < 0.001, 0.87 (0.87-0.88) p < 0.001 and 0.57 (0.56-0.57) p < 0.001, respectively). MLTC for persons aged 0-19 years were dominated by asthma, autism and epilepsy, for persons aged 20-49 years by depression and asthma, for persons aged 50-59 years by hypertension and depression and for those aged 60 years and older, by cardiometabolic factors and osteoarthritis. There were large numbers of combinations of conditions in each age group ranging from 5936 in those aged 0-19 years to 205,534 in those aged 80 years and over. CONCLUSIONS: While this study provides useful insight into the burden across the English population to assist health service delivery planning, the heterogeneity of MLTC presents challenges for delivery optimisation.

Addressing overestimation and insensitivity in the 85% target for average bed occupancy.

BACKGROUND: An established finding suggests that, in balancing variability in patient demand and length of stay, an average bed occupancy of 85% should be targeted for acute hospital wards. The notion is that higher figures result in excessive capacity breaches, while anything lower fails to make economic use of available resources. Although concerns have previously been raised regarding the generic use of the 85% target, there has been little research interest into alternative derivations that may better represent the diverse range of conditions that exist in practice. OBJECTIVE: To quantify a continuum of average occupancy targets for use within the acute hospital setting. METHODS: Computer simulation is used to model the process of acute patient admission and discharge. Patient arrivals are assumed to be independent of one another (i.e. random) with length of stay distributions obtained through fitting to patient-level data from all of England. RESULTS: Target average occupancy increases with ward size, ranging from 45% to 79% for a relatively small 15-bed ward to 64-84% for a relatively large 50-bed ward. Regarding ward speciality, for a typical 25-bed ward, values range from 57-58% for Gynaecology to 67-74% for Adult Mental Health. These increase to 62-63% and 75-82%, respectively, if the tolerance on breaching capacity is relaxed from 2% to 5% of days per year. CONCLUSION: An unconditional 85% target serves as an overestimate across the vast majority of settings that typically exist in practice. Hospital planners should consider ward size, speciality and capacity-breach tolerance in determining a more sensitive assessment of bed occupancy requirements. This study provides hospital planners with a means to reliably assess the operational performance and readily calculate optimal capacity requirements.

The impact of increased outpatient telehealth during COVID-19: Retrospective analysis of patient survey and routine activity data from a major healthcare system in England.

In response to societal restrictions due to the COVID-19 pandemic, a significant proportion of physical outpatient consultations were replaced with virtual appointments within the Bristol, North Somerset and South Gloucestershire healthcare system. The objective of this study was to assess the impact of this change in informing the potential viability of a longer-term shift to telehealth in the outpatient setting. A retrospective analysis was performed using data from the first COVID-19 wave, comprising 2998 telehealth patient surveys and 143,321 distinct outpatient contacts through both the physical and virtual medium. Four in five specialities showed no significant change in the overall number of consultations per patient during the first wave of the pandemic when telehealth services were widely implemented. Of those surveyed following virtual consultation, more respondents 'preferred' virtual (36.4%) than physical appointments (26.9%) with seven times as many finding them 'less stressful' than 'more stressful'. In combining both patient survey and routine activity data, this study demonstrates the importance of using data from multiple sources to derive useful insight. The results support the potential for telehealth to be rapidly employed across a range of outpatient specialities without negatively affecting patient experience.

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.

Dose-Response Modeling: Extrapolating From Experimental Data to Real-World Populations.

Dose-response modeling of biological agents has traditionally focused on describing laboratory-derived experimental data. Limited consideration has been given to understanding those factors that are controlled in a laboratory, but are likely to occur in real-world scenarios. In this study, a probabilistic framework is developed that extends Brookmeyer's competing-risks dose-response model to allow for variation in factors such as dose-dispersion, dose-deposition, and other within-host parameters. With data sets drawn from dose-response experiments of inhalational anthrax, plague, and tularemia, we illustrate how for certain cases, there is the potential for overestimation of infection numbers arising from models that consider only the experimental data in isolation.

Population Health Management to identify and characterise ongoing health need for high-risk individuals shielded from COVID-19: a cross-sectional cohort study.

OBJECTIVES: To use Population Health Management (PHM) methods to identify and characterise individuals at high-risk of severe COVID-19 for which shielding is required, for the purposes of managing ongoing health needs and mitigating potential shielding-induced harm. DESIGN: Individuals at 'high risk' of COVID-19 were identified using the published national 'Shielded Patient List' criteria. Individual-level information, including current chronic conditions, historical healthcare utilisation and demographic and socioeconomic status, was used for descriptive analyses of this group using PHM methods. Segmentation used k-prototypes cluster analysis. SETTING: A major healthcare system in the South West of England, for which linked primary, secondary, community and mental health data are available in a system-wide dataset. The study was performed at a time considered to be relatively early in the COVID-19 pandemic in the UK. PARTICIPANTS: 1 013 940 individuals from 78 contributing general practices. RESULTS: Compared with the groups considered at 'low' and 'moderate' risk (ie, eligible for the annual influenza vaccination), individuals at high risk were older (median age: 68 years (IQR: 55-77 years), cf 30 years (18-44 years) and 63 years (38-73 years), respectively), with more primary care/community contacts in the previous year (median contacts: 5 (2-10), cf 0 (0-2) and 2 (0-5)) and had a higher burden of comorbidity (median Charlson Score: 4 (3-6), cf 0 (0-0) and 2 (1-4)). Geospatial analyses revealed that 3.3% of rural and semi-rural residents were in the high-risk group compared with 2.91% of urban and inner-city residents (p<0.001). Segmentation uncovered six distinct clusters comprising the high-risk population, with key differentiation based on age and the presence of cancer, respiratory, and mental health conditions. CONCLUSIONS: PHM methods are useful in characterising the needs of individuals requiring shielding. Segmentation of the high-risk population identified groups with distinct characteristics that may benefit from a more tailored response from health and care providers and policy-makers.

Learning to keep your balance.

Balancing your work and your life proving impossible? Finding you have no time to offer yourself the care you offer others? Adrian Nelson-Pratt says the first step to rectifying the situation is to consider just how you are spending your days.

Time to change.

Adrian Nelson-Pratt explains how an understanding of the way in which you are spending your time makes it possible to claw back wasted hours and secure a better balance in your life.

Within-host mathematical modelling of the incubation period of Salmonella Typhi.

Mechanistic mathematical models are often employed to understand the dynamics of infectious diseases within a population or within a host. They provide estimates that may not be otherwise available. We have developed a within-host mathematical model in order to understand how the pathophysiology of Salmonella Typhi contributes to its incubation period. The model describes the process of infection from ingestion to the onset of clinical illness using a set of ordinary differential equations. The model was parametrized using estimated values from human and mouse experimental studies and the incubation period was estimated as 9.6 days. A sensitivity analysis was also conducted to identify the parameters that most affect the derived incubation period. The migration of bacteria to the caecal lymph node was observed as a major bottle neck for infection. The sensitivity analysis indicated the growth rate of bacteria in late phase systemic infection and the net population of bacteria in the colon as parameters that most influence the incubation period. We have shown in this study how mathematical models aid in the understanding of biological processes and can be used in estimating parameters of infectious diseases.