In 2015, approximately 60 000 people were diagnosed with head and neck cancer (HNC) in the United States, and 12 000 died of this cancer.^1,2 These cancers are predominately (>90%) squamous cell carcinomas (SCCs) arising from a variety of epithelial sites in the upper aerodigestive system, including the oral cavity, nasopharynx, oropharynx, hypopharynx, and larynx. The main risk factors for HNC are increasing age, male sex, cigarette smoking, and alcohol consumption.^3-7 More recently, alpha species human papillomavirus (α-HPV) infection has been identified as an important risk factor for head and neck squamous cell carcinoma (HNSCC), although there is considerable heterogeneity by tumor site.^6-10 Primarily, HPV-positive HNSCCs arise from the oropharyngeal region (ie, tonsils and base of the tongue)^11,12 and represent a subset of tumors that are diagnosed at an earlier age and are less likely, according to some studies, to be associated with cigarette smoking or alcohol consumption.^6,10,13-15 The majority (82%) of HPV-positive HNSCCs are due to HPV-16 infection, with an estimated prevalence of 41% in oropharyngeal SCCs and 13% to 15% in oral cavity and larynx SCCs.¹⁰

Parkinson disease (PD) and cancer have opposite biological mechanisms: PD is characterized by apoptosis and premature neuronal degeneration, and the hallmark of cancer is uncontrolled cell proliferation.¹ However, a link between PD and cancer was suspected when higher incidence rates of melanoma were observed among patients with PD.² The excess melanoma risk might be the result of a shared relationship between tyrosinase and melanin, but not levodopa treatment.^3-6 The overexpression of α-synuclein leads to cell degeneration in the brain. In the skin the overexpression may inhibit tyrosinase and tyrosine hydroxylase and thus decrease the levels of protective melanin.³ In turn, the lower melanin levels could increase a person’s susceptibility to the deleterious effects of ionizing radiation and environmental toxins leading to melanoma.³ Family members of patients with PD are more likely to develop melanoma, and patients with melanoma and their family members have an increased risk of PD.^7-9

The follow-up rate, a standard index of the completeness of follow-up, is important for assessing the validity of a cohort study. A common method for estimating the follow-up rate, the “Percentage Method”, defined as the fraction of all enrollees who developed the event of interest or had complete follow-up, can severely underestimate the degree of follow-up. Alternatively, the median follow-up time does not indicate the completeness of follow-up, and the reverse Kaplan-Meier based method and Clark’s Completeness Index (CCI) also have limitations. We propose a new definition for the follow-up rate, the Person-Time Follow-up Rate (PTFR), which is the observed person-time divided by total person-time assuming no dropouts. The PTFR cannot be calculated directly since the event times for dropouts are not observed. Therefore, two estimation methods are proposed: a formal person-time method (FPT) in which the expected total follow-up time is calculated using the event rate estimated from the observed data, and a simplified person-time method (SPT) that avoids estimation of the event rate by assigning full follow-up time to all events. Simulations were conducted to measure the accuracy of each method, and each method was applied to a prostate cancer recurrence study dataset. Simulation results showed that the FPT has the highest accuracy overall. In most situations, the computationally simpler SPT and CCI methods are only slightly biased. When applied to a retrospective cohort study of cancer recurrence, the FPT, CCI and SPT showed substantially greater 5-year follow-up than the Percentage Method (92%, 92% and 93% vs 68%). The Person-time methods correct a systematic error in the standard Percentage Method for calculating follow-up rates. The easy to use SPT and CCI methods can be used in tandem to obtain an accurate and tight interval for PTFR. However, the FPT is recommended when event rates and dropout rates are high.

Background: Severe coronavirus disease 2019 (COVID‐19) is characterized by a proinflammatory state with high mortality. Statins have anti‐inflammatory effects and may attenuate the severity of COVID‐19. Methods and Results: An observational study of all consecutive adult patients with COVID‐19 admitted to a single center located in Bronx, New York, was conducted from March 1, 2020, to May 2, 2020. Patients were grouped as those who did and those who did not receive a statin, and in‐hospital mortality was compared by competing events regression. In addition, propensity score matching and inverse probability treatment weighting were used in survival models to examine the association between statin use and death during hospitalization. A total of 4252 patients were admitted with COVID‐19. Diabetes mellitus modified the association between statin use and in‐hospital mortality. Patients with diabetes mellitus on a statin (n=983) were older (69±11 versus 67±14 years; P <0.01), had lower inflammatory markers (C‐reactive protein, 10.2; interquartile range, 4.5–18.4 versus 12.9; interquartile range, 5.9–21.4 mg/dL; P <0.01) and reduced cumulative in‐hospital mortality (24% versus 39%; P <0.01) than those not on a statin (n=1283). No difference in hospital mortality was noted in patients without diabetes mellitus on or off statin (20% versus 21%; P =0.82). Propensity score matching (hazard ratio, 0.88; 95% CI, 0.83–0.94; P <0.01) and inverse probability treatment weighting (HR, 0.88; 95% CI, 0.84–0.92; P <0.01) showed a 12% lower risk of death during hospitalization for statin users than for nonusers. Conclusions: Statin use was associated with reduced in‐hospital mortality from COVID‐19 in patients with diabetes mellitus. These findings, if validated, may further reemphasize administration of statins to patients with diabetes mellitus during the COVID‐19 era.