Prevalence of chronic comorbidities in dengue fever and West Nile virus: A systematic review and meta-analysis

<div><p>Background</p><p>Flavivirus diseases such as dengue fever (DENV), West Nile virus (WNV), Zika and yellow fever represent a substantial global public health concern. Preexisting chronic conditions such as cardiovascular diseases, diabetes, obesity, and asthma were thought to predict risk of progression to severe infections.</p><p>Objective</p><p>We aimed to quantify the frequency of chronic comorbidities in flavivirus diseases to provide an estimate for their prevalence in severe and non-severe infections and examine whether chronic diseases contribute to the increased risk of severe viral expression.</p><p>Methods</p><p>We conducted a comprehensive search in PubMed, Ovid MEDLINE(R), Embase and Embase Classic and grey literature databases to identify studies reporting prevalence estimates of comorbidities in flavivirus diseases. Study quality was assessed with the risk of bias tool. Age-adjusted odds ratios (ORs) were estimated for severe infection in the presence of chronic comorbidities.</p><p>Results</p><p>We identified 65 studies as eligible for inclusion for DENV (47 studies) and WNV (18 studies). Obesity and overweight (i.e., BMI> 25 kg/m², prevalence: 24.5%, 95% CI: 18.6–31.6%), hypertension (17.1%, 13.3–21.8%) and diabetes (13.3%, 9.3–18.8%) were the most prevalent comorbidities in DENV. However, hypertension (45.0%, 39.1–51.0%), diabetes (24.7%, 20.2–29.8%) and heart diseases (25.6%, 19.5–32.7%) were the most prevalent in WNV. ORs of severe flavivirus diseases were about 2 to 4 in infected patients with comorbidities such as diabetes, hypertension and heart diseases. The small number of studies in JEV, YFV and Zika did not permit estimating the prevalence of comorbidities in these infections.</p><p>Conclusion</p><p>Higher prevalence of chronic comorbidities was found in severe cases of flavivirus diseases compared to non-severe cases. Findings of the present study may guide public health practitioners and clinicians to evaluate infection severity based on the presence of comorbidity, a critical public health measure that may avert severe disease outcome given the current dearth of clear prevention practices for some flavivirus diseases.</p></div

Pooled prevalence estimates of comorbidities in severe and non-severe cases of flavivirus infections dengue fever and West Nile virus.

<p>Pooled prevalence estimates of comorbidities in severe and non-severe cases of flavivirus infections dengue fever and West Nile virus.</p

Keywords for the search related terms and synonyms.

<p>Keywords for the search related terms and synonyms.</p

Meta-analysis for the prevalence of clinical symptoms in flavivirus infections in the selected studies.

<p>Meta-analysis for the prevalence of clinical symptoms in flavivirus infections in the selected studies.</p

Meta-analysis for the proportion of comorbidities in dengue fever cases.

<p>Weights are calculated from binary random-effects model analysis. Values represent proportion of diabetes (a), hypertension (b), heart diseases (c), asthma (d), stroke (e) and obesity/overweight (f) in dengue fever patients and 95% CI. Heterogeneity analysis was carried out using <i>Q</i> test, the among studies variation (<i>I</i>^<i>2</i> index) and within-study variance in the random-effects model (τ²).</p

Meta-analysis for the frequency of clinical features in severe flavivirus infections in the selected studies.

<p>Meta-analysis for the frequency of clinical features in severe flavivirus infections in the selected studies.</p

Flowchart of study selection and systematic literature review process.

<p>The flow diagram describes the systematic review of literature on the prevalence of comorbidities in flavivirus infections. A total of 65 unique studies were identified (47 studies for dengue fever and 18 for West Nile virus from an initial 1373 examined titles). aSome studies reported on more than one flavivirus disease. Studies drawn from the same population were not included in the meta-analyses.</p

Characteristics of studies included in meta-analysis for prevalence of chronic comorbidities in flavivirus infections.

<p>Characteristics of studies included in meta-analysis for prevalence of chronic comorbidities in flavivirus infections.</p

Supplementary materials: Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer

These are peer-reviewed supplementary materials for the article 'Augmenting external control arms using Bayesian borrowing: a case study in first-line non-small cell lung cancer' published in the Journal of Comparative Effectiveness Research.Supplementary figure 1aSupplementary figure 1bSupplementary figure 2aSupplementary figure 2bSupplementary figure 3Supplementary table 1Supplementary table 2Supplementary table 3Supplementary table 4Supplementary table 5Supplementary table 6Aim: This study aimed to improve comparative effectiveness estimates and discuss challenges encountered through the application of Bayesian borrowing (BB) methods to augment an external control arm (ECA) constructed from real-world data (RWD) using historical clinical trial data in first-line non-small-cell lung cancer (NSCLC). Materials & methods: An ECA for a randomized controlled trial (RCT) in first-line NSCLC was constructed using ConcertAI Patient360™ to assess chemotherapy with or without cetuximab, in the bevacizumab-inappropriate subpopulation. Cardinality matching was used to match patient characteristics between the treatment arm (cetuximab + chemotherapy) and ECA. Overall survival (OS) was assessed as the primary outcome using Cox proportional hazards (PH). BB was conducted using a static power prior under a Weibull PH parameterization with borrowing weights from 0.0 to 1.0 and augmentation of the ECA from a historical control trial. Results: The constructed ECA yielded a higher overall survival (OS) hazard ratio (HR) (HR = 1.53; 95% CI: 1.21–1.93) than observed in the matched population of the RCT (HR = 0.91; 95% CI: 0.73–1.13). The OS HR decreased through the incorporation of BB (HR = 1.30; 95% CI: 1.08–1.54, borrowing weight = 1.0). BB was applied to augment the RCT control arm via a historical control which improved the precision of the observed HR estimate (1.03; 95% CI: 0.86–1.22, borrowing weight = 1.0), in comparison to the matched population of the RCT alone. Conclusion: In this study, the RWD ECA was unable to successfully replicate the OS estimates from the matched population of the selected RCT. The inability to replicate could be due to unmeasured confounding and variations in time-periods, follow-up and subsequent therapy. Despite these findings, we demonstrate how BB can improve precision of comparative effectiveness estimates, potentially aid as a bias assessment tool and mitigate challenges of traditional methods when appropriate external data sources are available.</p