The number of people affected by chronic obstructive pulmonary disease (COPD) is increasing and the hospital readmission rate is remarkably high. Therefore, healthcare professionals and managers have financial and workforce-related pressures. A decision support toolkit (DST) for improving the management and efficiency of COPD care is needed to respond to the needs of patients now and in the future. In collaboration with the COPD team of a hospital and community service in London, we conceptualised the pathway for COPD patients and developed a discrete event simulation model (DES) incorporating the dynamics of patient readmissions. A DES model or operational model at this scale has never been previously developed, despite many studies using other modelling and simulation techniques in COPD. Our model is the first of its kind to include COPD readmissions as well as assessing the quantifiable impact of re-designing COPD services. We demonstrate the impact of post-exacerbation pulmonary rehabilitation (PEPR) policy and observe that PEPR would be cost-effective with improvements in quality-adjusted life years (QALYs), reduction in emergency readmissions and occupied bed days. The DST improves the understanding of the impact of scenarios (activities, resources, financial implications etc.) for key decision makers and supports commissioners in implementing the interventions.
Objective To assess the impact of interventions for improving the management of chronic obstructive pulmonary disease (COPD), specifically increased use of pulmonary rehabilitation (PR) on patient outcomes and cost‐benefit analysis. Data Sources We used the national Hospital Episode Statistics (HES) datasets in England, local data and experts from the hospital setting, National Prices and National Tariffs, reports and the literature around the effectiveness of PR programs. Study Design The COPD pathway was modeled using discrete event simulation (DES) to capture the patient pathway to an adequate level of detail as well as randomness in the real world. DES was further enhanced by the integration of a health economic model to calculate the net benefit and cost of treating COPD patients based on key sets of interventions. Data Collection/Extraction Methods A total of 150 input parameters and 75 distributions were established to power the model using the HES dataset, outpatient activity data from the hospital and community services, and the literature. Principal Findings The simulation model showed that increasing referral to PR (by 10%, 20%, or 30%) would be cost‐effective (with a benefit‐cost ratio of 5.81, 5.95, and 5.91, respectively) by having a positive impact on patient outcomes and operational metrics. Number of deaths, admissions, and bed days decreased (ie, by 3.56 patients, 4.90 admissions, and 137.31 bed days for a 30% increase in PR referrals) as well as quality of life increased (ie, by 5.53 QALY among 1540 patients for the 30% increase). Conclusions No operational model, either statistical or simulation, has previously been developed to capture the COPD patient pathway within a hospital setting. To date, no model has investigated the impact of PR on COPD services, such as operations, key performance, patient outcomes, and cost‐benefit analysis. The study will support policies around extending availability of PR as a major intervention.
Background Background Eleven out of 13 published articles reported temperature and humidity as factors that could reduce the daily confirmed COVID-19 cases among many other findings. However, there are significant caveats, related to statistical assumptions and the spatial-temporal nature of the data. Methods Methods Associative and causative analyses of data was conducted for 10 countries representing 6 continents of the world, with data obtained between January 22, 2020 to April 30, 2020. Daily confirmed cases, number of deaths, recovered cases, lockdown stringency index, and several meteorological factors are considered. Also, a Granger-Causality test was performed to check if any COVID-19 outcomes are influenced by itself and not by any or combination of maximum temperature, humidity, wind speed and stringency index. Results Results Most of the associations reported in the literature, between meteorological parameters and COVID-19 pandemic are weak evidence, need to be interpreted with caution, as most of these articles neglected the temporal spatial nature of the data. Based on our findings, most of the correlations no matter which coefficient is used are mostly and strictly between-0.5 and 0.5, and these are weak correlations. An interesting finding is the correlation between stringency and each of the COVID-19 outcomes, the strongest being between stringency and confirmed cases, 0.80 (0.78, 0.82) P<.0001. Similarly, wind speed is weakly associated with recovery rate, 0.22 (0.16, 0.28) P<.0001. Lastly, the Granger-Causality test of no dependencies was accepted at P=0.1593, suggesting independence among the parameters. Conclusions Conclusions Although many articles reported association between meteorological parameters and COVID-19, they mainly lack strong evidence and clear interpretation of the statistical results (e.g. underlying assumption, confidence intervals, a clear hypothesis). Our findings showed that, without effective control measures, strong outbreaks are likely in more windy climates and summer weather, humidity or warmer temperature will not substantially limit pandemic growth.
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