Effectiveness of a population-based integrated care model in reducing hospital activity: an interrupted time series analysis.

Objectives: First impact assessment analysis of an integrated care model (ICM) to reduce hospital activity in the London Borough of Hillingdon, UK. Methods: We evaluated a population-based ICM consisting of multiple interventions based on self-management, multidisciplinary teams, case management and discharge management. The sample included 331 330 registered Hillingdon residents (at the time of data extraction) between October 2018 and July 2020. Longitudinal data was extracted from the Whole Systems Integrated Care database. Interrupted time series Poisson and Negative binomial regressions were used to examine changes in non-elective hospital admissions (NEL admissions), accident and emergency visits (A&E) and length of stay (LoS) at the hospital. Multiple imputations were used to replace missing data. Subgroup analysis of various groups with and without long-term conditions (LTC) was also conducted using the same models. Results: In the whole registered population of Hillingdon at the time of data collection, gradual decline over time in NEL admissions (RR 0.91, 95% CI 0.90 to 0.92), A&E visits (RR 0.94, 95% CI 0.93 to 0.95) and LoS (RR 0.93, 95% CI 0.92 to 0.94) following an immediate increase during the first months of implementation in the three outcomes was observed. Subgroup analysis across different groups, including those with and without LTCs, showed similar effects. Sensitivity analysis did not show a notable change compared with the original analysis. Conclusion: The Hillingdon ICM showed effectiveness in reducing NEL admissions, A&E visits and LoS. However, further investigations and analyses could confirm the results of this study and rule out the potential effects of some confounding events, such as the emergence of COVID-19 pandemic.

CALMS: Modelling the long-term health and economic impact of Covid-19 using agent-based simulation.

We present our agent-based CoronAvirus Lifelong Modelling and Simulation (CALMS) model that aspires to predict the lifelong impacts of Covid-19 on the health and economy of a population. CALMS considers individual characteristics as well as comorbidities in calculating the risk of infection and severe disease. We conduct two sets of experiments aiming at demonstrating the validity and capabilities of CALMS. We run simulations retrospectively and validate the model outputs against hospitalisations, ICU admissions and fatalities in a UK population for the period between March and September 2020. We then run simulations for the lifetime of the cohort applying a variety of targeted intervention strategies and compare their effectiveness against the baseline scenario where no intervention is applied. Four scenarios are simulated with targeted vaccination programmes and periodic lockdowns. Vaccinations are targeted first at individuals based on their age and second at vulnerable individuals based on their health status. Periodic lockdowns, triggered by hospitalisations, are tested with and without vaccination programme in place. Our results demonstrate that periodic lockdowns achieve reductions in hospitalisations, ICU admissions and fatalities of 6-8% compared to the baseline scenario, with an associated intervention cost of £173 million per 1,000 people and targeted vaccination programmes achieve reductions in hospitalisations, ICU admissions and fatalities of 89-90%, compared to the baseline scenario, with an associated intervention cost of £51,924 per 1,000 people. We conclude that periodic lockdowns alone are ineffective at reducing health-related outputs over the long-term and that vaccination programmes which target only the clinically vulnerable are sufficient in providing healthcare protection for the population as a whole.

Determinants of COVID-19-Related Length of Hospital Stays and Long COVID in Ghana: A Cross-Sectional Analysis.

OBJECTIVES: There is paucity of data on determinants of length of COVID-19 admissions and long COVID, an emerging long-term sequel of COVID-19, in Ghana. Therefore, this study identified these determinants and discussed their policy implications. METHOD: Data of 2334 patients seen at the main COVID-19 treatment centre in Ghana were analysed in this study. Their characteristics, such as age, education level and comorbidities, were examined as explanatory variables. The dependent variables were length of COVID-19 hospitalisations and long COVID. Negative binomial and binary logistic regressions were fitted to investigate the determinants. RESULT: The regression analyses showed that, on average, COVID-19 patients with hypertension and diabetes mellitus spent almost 2 days longer in hospital (p = 0.00, 95% CI = 1.42-2.33) and had 4 times the odds of long COVID (95% CI = 1.61-10.85, p = 0.003) compared to those with no comorbidities. In addition, the odds of long COVID decreased with increasing patient's education level (primary OR = 0.73, p = 0.02; secondary/vocational OR = 0.26, p = 0.02; tertiary education OR = 0.23, p = 0.12). CONCLUSION: The presence of hypertension and diabetes mellitus determined both length of hospitalisation and long COVID among patients with COVID-19 in Ghana. COVID-19 prevention and management policies should therefore consider these factors.