Influence of chronic hepatitis C infection on the monocyte-to-platelet ratio: data analysis from the National Health and Nutrition Examination Survey (2009–2016)

Background Hepatitis C virus (HCV) causes life-threatening chronic infections. Implementation of novel, economical or widely available screening tools can help detect unidentified cases and facilitate their linkage to care. We investigated the relationship between chronic HCV infection and a potential complete blood count biomarker (the monocyte-to-platelet ratio) in the United States. Methods The analytic dataset was selected from cycle years 2009–2016 of the National Health and Nutrition Examination Survey. Complete case data- with no missingness- was available for n = 5281 observations, one-hundred and twenty-two (n = 122) of which were exposed to chronic HCV. The primary analysis used survey-weighted logistic regression to model the effect of chronic HCV on the monocyte-to-platelet ratio adjusting for demographic and biological confounders in a causal inference framework. Missing data and propensity score methods were respectively performed as a secondary and sensitivity analysis. Results In the analytic dataset, outcome data was available for n = 5281 (n = 64,245,530 in the weighted sample) observations of which n = 122 (n = 1,067,882 in the weighted sample) tested nucleic acid positive for HCV. Those exposed to chronic HCV infection in the United States have 3.10 times the odds of a high monocyte-to-platelet ratio than those not exposed (OR = 3.10, [95% CI: 1.55–6.18]). Conclusion A relationship exists between chronic HCV infection and the monocyte-to-platelet ratio in the general population of the United States. Reversing the direction of this association to predict chronic HCV infection from complete blood counts, could provide an economically feasible and universal screening tool, which would help link patients with care.

Variation in Tularemia Clinical Manifestations—Arkansas, 2009–2013

Background Francisella tularensis, although naturally occurring in Arkansas, is also a Tier 1 select agent and potential bioterrorism threat. As such, tularemia is nationally notifiable and mandatorily reported to the Arkansas Department of Health. We examined demographic and clinical characteristics among reported cases and outcomes to improve understanding of the epidemiology of tularemia in Arkansas. Methods Surveillance records on all tularemia cases investigated during 2009–2013 were reviewed. Results The analytic dataset was assembled from 284 tularemia reports, yielding 138 probable and confirmed tularemia cases during 2009–2013. Arthropod bite was identified in 77% of cases. Of 7 recognized tularemia manifestations, the typhoidal form was reported in 47% of cases, approximately double the proportion of the more classic manifestation, lymphadenopathy. Overall, 41% of patients were hospitalized; 3% died. The typhoidal form appeared to be more severe, accounting for the majority of sepsis and meningitis cases, hospitalizations, and deaths. Among patients with available antibiotic data, 88% received doxycycline and 12% received gentamicin. Conclusions Contrary to expectation, lymphadenopathy was not the most common manifestation observed in our registry. Instead, our patients were more likely to report only generalized typhoidal symptoms. Using lymphadenopathy as a primary symptom to initiate tularemia testing may be an insensitive diagnostic strategy and result in unrecognized cases. In endemic areas such as Arkansas, suspicion of tularemia should be high, especially during tick season. Outreach to clinicians describing the full range of presenting symptoms may help address misperceptions about tularemia.

Meta-Analysis with Heteroscedastic Effects

To enhance the utility of meta-analysis as an integrative tool for marketing research, heteroscedastic MLE (HMLE), a maximum-likelihood-based estimation procedure, is proposed as a method that overcomes heteroscedasticity, a problem known to impair OLS estimates and threaten the validity of meta-analytic findings. The results of a Monté Carlo simulation experiment reveal that, under a wide range of heteroscedastic conditions, HMLE is more efficient and powerful than OLS and achieves these performance advantages without inflating type I error. Further, the relative performance of HMLE increases as heteroscedasticity becomes more severe. An empirical analysis of a meta-analytic dataset in marketing confirmed and extended these findings by illustrating how the enhanced efficiency and power of HMLE improve the ability to detect moderator variables and by demonstrating how the theoretical generalizations emerging from a meta-analysis are affected by the choice of the analytic procedure.