Benefit-Harm Analysis of Earlier Initiation of Triple Therapy for Prevention of Acute Exacerbation in Patients with COPD.

Rationale: Reducing the risk of exacerbation is a fundamental goal in managing stable COPD. Guidelines recommend triple therapy (inhaled corticosteroids, long-acting muscarinic antagonists, and long-acting beta-agonists [ICS/LAMA/LABA]) only as a step-up from dual therapy (LAMA/LABA) for patients at continued high risk of exacerbation, due to the trade-off of an increased risk of pneumonia associated with ICS-containing therapies. However, there is little evidence on the optimum timing of initiating triple therapy. Objectives: To perform a benefit-harm analysis to evaluate the net benefit of earlier initiation of triple therapy for the prevention of acute exacerbations in patients with COPD, compared to standard timing recommended in current guidelines. Methods: We used a validated whole disease microsimulation model of COPD in the Canadian general population aged ≥40 years to determine the benefit-harm of earlier initiation of triple therapy over a 20-year time horizon, compared to standard care. We assessed net change in quality-adjusted life-years (QALYs) from the reduction in risk of acute exacerbations and the increased risk of treatment-related pneumonia in subgroups of patients with COPD defined by exacerbation history, symptoms, and disease severity. Model parameters were determined from clinical trials and other published literature. Key parameters were varied in one-way sensitivity analysis. Results: In patients at high risk of acute exacerbation (54.7% female, mean age 74.0, 68% GOLD grade I-II), earlier initiation of triple therapy was associated with a net QALY gain of 4.8 per 100 COPD patients over 20 years, compared to standard care. The net QALY gain increased to 5.9 per 100 patients in the subgroup of patients with a high symptom burden (mMRC>1). Earlier initiation remained net beneficial in all subgroup and sensitivity analysis scenarios. Conclusions: Modeling suggests that earlier initiation of triple therapy is likely to be net beneficial for patients at high risk of acute exacerbation, with an even greater benefit to patients with a high symptom burden. Further clinical research is needed to verify these findings in empirical studies.

Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England.

BACKGROUND: Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. METHODS: Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. RESULTS: The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. CONCLUSIONS: Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care.

Budget impact analysis of adopting primary care-based case detection of chronic obstructive pulmonary disease in the Canadian general population.

BACKGROUND: An estimated 70% of Canadians with chronic obstructive pulmonary disease (COPD) have not received a diagnosis, creating a barrier to early intervention, and there is growing interest in the value of primary care-based opportunistic case detection for COPD. We sought to build on a previous cost-effectiveness analysis by evaluating the budget impact of adopting COPD case detection in the Canadian general population. METHODS: We used a validated discrete-event microsimulation model of COPD in the Canadian general population aged 40 years and older to assess the costs of implementing 8 primary care-based case detection strategies over 5 years (2022-2026) from the health care payer perspective. Strategies varied in eligibility criteria (based on age, symptoms or smoking history) and testing technology (COPD Diagnostic Questionnaire [CDQ] or screening spirometry). Costs were determined from Canadian studies and converted to 2021 Canadian dollars. Key parameters were varied in one-way sensitivity analysis. RESULTS: All strategies resulted in higher total costs compared with routine diagnosis. The most cost-effective scenario (the CDQ for all patients) had an associated total budget expansion of $423 million, with administering case detection and subsequent diagnostic spirometry accounting for 86% of costs. This strategy increased the proportion of individuals diagnosed with COPD from 30.4% to 37.8%, and resulted in 4.6 million referrals to diagnostic spirometry. Results were most sensitive to uptake in primary care. INTERPRETATION: Adopting a national COPD case detection program would be an effective method for increasing diagnosis of COPD, dependent on successful uptake. However, it will require prioritisation by budget holders and substantial additional investment to improve access to diagnostic spirometry.

Socioeconomic and health factors related to polypharmacy and medication management: analysis of a Household Health Survey in North West Coast England.

OBJECTIVES: To examine the socioeconomic and demographic drivers associated with polypharmacy (5-9 medicines), extreme polypharmacy (9-20 medicines) and increased medication count. DESIGN, SETTING AND PARTICIPANTS: A total of 5509 participants, from two waves of the English North West Coast, Household Health Survey were analysed OUTCOME MEASURES: Logistic regression modelling was used to find associations with polypharmacy and extreme polypharmacy. A negative binomial regression identified associations with increased medication count. Descriptive statistics explored associations with medication management. RESULTS: Age and number of health conditions account for the greatest odds of polypharmacy. ORs (95% CI) were greatest for those aged 65+ (3.87, 2.45 to 6.13) and for those with ≥5 health conditions (10.87, 5.94 to 19.88). Smaller odds were seen, for example, in those prescribed cardiovascular medications (3.08, 2.36 to 4.03), or reporting >3 emergency attendances (1.97, 1.23 to 3.17). Extreme polypharmacy was associated with living in a deprived neighbourhood (1.54, 1.06 to 2.26). The greatest risk of increased medication count was associated with age, number of health conditions and use of primary care services. Relative risks (95% CI) were greatest for those aged 65+ (2.51, 2.23 to 2.82), those with ≥5 conditions (10.26, 8.86 to 11.88) or those reporting >18 primary care visits (2.53, 2.18 to 2.93). Smaller risks were seen in, for example, respondents with higher levels of income deprivation (1.35, 1.03 to 1.77). Polypharmic respondents were more likely to report medication management difficulties associated with taking more than one medicine at a time (p<0.001). Furthermore, individuals reporting a mental health condition, were significantly more likely to consistently report difficulties managing their medication (p<0.001). CONCLUSION: Age and number of health conditions are most associated with polypharmacy. Thus, delaying or preventing the onset of long-term conditions may help to reduce polypharmacy. Interventions to reduce income inequalities and health inequalities generally could support a reduction in polypharmacy, however, more research is needed in this area. Furthermore, increased prevention and support, particularly with medication management, for those with mental health conditions may reduce adverse medication effects.

Recruitment prediction for multicenter clinical trials based on a hierarchical Poisson-gamma model: Asymptotic analysis and improved intervals.

We analyze predictions of future recruitment to a multicenter clinical trial based on a maximum-likelihood fitting of a commonly used hierarchical Poisson-gamma model for recruitments at individual centers. We consider the asymptotic accuracy of quantile predictions in the limit as the number of recruitment centers grows large and find that, in an important sense, the accuracy of the quantiles does not improve as the number of centers increases. When predicting the number of further recruits in an additional time period, the accuracy degrades as the ratio of the additional time to the census time increases, whereas when predicting the amount of additional time to recruit a further n•+$n^+_\bullet$ patients, the accuracy degrades as the ratio of n•+$n^+_\bullet$ to the number recruited up to the census period increases. Our analysis suggests an improved quantile predictor. Simulation studies verify that the predicted pattern holds for typical recruitment scenarios in clinical trials and verify the much improved coverage properties of prediction intervals obtained from our quantile predictor. In the process of extending the applicability of our methodology, we show that in terms of the accuracy of all integer moments it is always better to approximate the sum of independent gamma random variables by a single gamma random variable matched on the first two moments than by the moment-matched Gaussian available from the central limit theorem.