Objectives We conducted a systematic review and meta-analysis to determine the magnitude of infection risk in patients with SLE and evaluate the effect of general and SLE-related factors on infection risk. Methods We searched MEDLINE and Embase from inception to July 2018, screening for observational studies that evaluated infection risk in patients with SLE compared with the general population/healthy controls. Outcomes of interest included overall severe infection, herpes zoster infection/reactivation, opportunistic infections, pneumonia and tuberculosis. Random-effects models were used to calculate pooled risk ratios (RRs) for each type of infection. Sensitivity analysis assessed the impact of removing studies with high risk of bias. Results Eleven retrospective or prospective cohort studies were included in the meta-analysis: overall severe infection (n = 4), pneumonia (n = 6), tuberculosis (n = 3) and herpes zoster (n = 2). Pooled RRs for overall severe infection significantly increased for patients with SLE compared with the general population/healthy controls [RR 2.96 (95% CI 1.28, 6.83)]. Pooled RRs for pneumonia, herpes zoster and tuberculosis showed significantly increased risk compared with the general population/healthy controls [RR 2.58 (1.80, 3.70), 2.50 (2.36, 2.65) and 6.11 (3.61, 10.33), respectively]. Heterogeneity and evidence of publication bias were present for all analyses, except herpes zoster. Sensitivity analyses confirmed robustness of the results. Conclusion Patients with SLE have significantly higher risk of infection compared with the general population/healthy controls. Efforts to strengthen strategies aimed at preventing infections in SLE are needed. Protocol registration PROSPERO number: CRD42018109425.

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Background: The majority of patients with chronic obstructive pulmonary disease (COPD) suffer from comorbid cardiovascular (CV) disease. Accumulating evidence suggests a temporal association between COPD exacerbations and acute CV events, possibly due to lung hyperinflation, increased hypoxemia and systemic inflammation. The aims of the study were to estimate the risk of (1) acute CV events [acute myocardial infarction (AMI), CV-related death] or stroke in the months following a COPD exacerbation and (2) COPD exacerbation in the months following an acute CV event. Methods: A systematic literature review of observational studies published since 2000 was conducted by searching literature databases (Medline and Embase). Studies were eligible if conducted in adults with COPD, exposed to either COPD exacerbation or acute CV events, with outcomes of acute CV events or COPD exacerbation reported. Studies were appraised for relevance, bias and quality. Meta-analyses, using random-effect models, were performed for each outcome of interest, thus providing a pooled relative risk (RR) and its 95% confidence interval. Results: Eight studies were identified, of which seven were used for the meta-analyses examining the risk of CV events 1–3 months after an exacerbation compared with none. For stroke (six studies), RR was 1.68 (95% CI = 1.19–2.38). For AMI (six studies), RR was 2.43 (95% CI = 1.40–4.20). No studies exploring risk of exacerbation following an acute CV event were identified. Conclusion: This meta-analysis identified a markedly increased risk of stroke or AMI within a relatively short period of time following a COPD exacerbation. Although the underlying mechanisms are not fully elucidated, patients with COPD should be monitored for risk of CV outcomes after exacerbations. In addition, preventing exacerbations may decrease the risk of subsequent acute CV events. Registration: The study protocol was published via PROSPERO: International Prospective Register of Systematic Reviews (#CRD42020211055).

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In the United States (US), payer listing of new technology and healthcare decision-making is often supported by budget impact information and economic burden value-propositions. Budget impact models created for the US often present results using the per-member per-month (PMPM) expenditure. It is not a simple matter to determine what is a good or bad PMPM value. The goal of this review was to examine the factors affecting PMPM costs and to determine the distribution of PMPM costs in oncology. Methods: A targeted literature search was conducted, PMPM data from studies (published between 2013-2019) on different types of malignancies were extracted. PMPM results were evaluated and compared to identify factors influencing their values. All included studies were designed for US payer perspective. Results: From fifty-one studies initially identified, 22 met the inclusion criteria. Health-plan members were generally estimated at 1-million-members and the time horizon varied between one and five years. Of the reported PMPM, 32% were in lung cancer, followed by 14% in prostate cancer, and 14% in hemato-oncology. Other than the foundational cost of the drug and incidence in the population for PMPM calculation, factors influencing PMPM value were type of disease, time horizon, market share, survival after new treatment, baseline characteristic of the population, and geographical variables. Additionally, it was determined that noncurative new treatments with prolonged survival led to higher PMPM. There was considerable usage of PMPM for screening procedures with the intention to show usefulness or cost-savings. Conclusions: To our knowledge, this is the first literature review of studies which reported PMPM to measure the expenditures of the intervention in the field of oncology. The PMPM should not be interpreted without considering the context, baseline characteristics and underlying diseases play a significant role in determining acceptable PMPM.