Faculty Bibliography

BACKGROUND:Ultraviolet radiation (UVR) exposure is the primary risk factor for melanoma although the relationship is complex. Compared to radiation from UVB wavelengths, UVA makes up a majority of the surface solar UVR, penetrates the skin more deeply, is the principal range emitted by tanning beds, and is less filtered by sunscreens and window glass. Few studies have examined the relationship between ambient UVA and UVB and melanoma risk. METHODS:Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated for the association between satellite-based ambient (based on residential history) UVA, UVB and melanoma in non-Hispanic White participants using data from the United States Radiologic Technologists study, a large, nationwide prospective cohort. Associations of UVA and UVB quartile (Q) were examined in mutually adjusted and stratified models, additionally adjusted for demographic and sun sensitivity characteristics. RESULTS:There were 837 incident melanoma cases among 62,785 participants. Incidence of melanoma was statistically significantly increased for the highest quartile of childhood UVA exposure after adjustment for UVB (IRR = 2.82; 95%CI:1.46,5.44), but not for higher childhood UVB after adjustment for UVA. Childhood UVA was associated with increased melanoma risk within strata of UVB. Childhood UVB was not associated with melanoma after adjustment for UVA, but there was some evidence of lower risk with increased lifetime ambient UVB after UVA adjustment. CONCLUSIONS:Melanoma risk was elevated among participants living in locations with high annual childhood and lifetime UVA after controlling for UVB. With confirmation, these findings support increased protection from solar UVA for melanoma prevention.

BACKGROUND/UNASSIGNED:Low dose oral minoxidil (LDOM) is a preferred treatment for alopecia due to ease of use and efficacy. While LDOM is typically well tolerated, patients may experience a temporary increase in hair shedding starting treatment, colloquially regarded as "dread shed". One proposed method to combat this is to overlap therapies by maintaining use of topical minoxidil when initiating LDOM. OBJECTIVE/UNASSIGNED:To evaluate the impact of maintaining topical minoxidil when initiating LDOM on "dread shed". METHODS/UNASSIGNED:We performed a retrospective chart review of patients seen at New York University Langone Health Dermatology from January 1, 2008 to August 1, 2023 prescribed LDOM. RESULTS/UNASSIGNED:A total of 115 patients met inclusion criteria, of whom 37 maintained use of topical minoxidil when initiating LDOM. Six patients experienced "dread shed" when initiating LDOM, 2 of whom overlapped therapies. We did not find that overlapping therapies had a significant impact on decreasing rates of "dread shed". LIMITATIONS/UNASSIGNED:Limitations include retrospective design, sample size, and subjective patient-reported assessment of hair shedding. CONCLUSIONS/UNASSIGNED:A total of 5.2% of patients experienced dread shed, which is lower than previously reported in literature. Maintaining topical minoxidil during LDOM initiation does not significantly impact "dread shed". This remains a significant side effect deserving of further research.

Low dose oral minoxidil (LDOM) is an efficacious and safe treatment for alopecia, however, a notable side effect is hypertrichosis. Spironolactone, known for treating hirsutism, is also used off-label for the treatment of certain forms of alopecia and may reduce LDOM-induced hypertrichosis. We performed a retrospective review of 54 patients seen at NYU Langone Health and compared hypertrichosis rates in female alopecia patients on LDOM monotherapy versus those on combination therapy with spironolactone. Among 54 patients, 37 received LDOM alone and 17 received the combination. Hypertrichosis developed in 33.3% of patients, with lower rates in the combination group (17.6% vs. 40.5% for monotherapy). Although not statistically significant, the trend suggests spironolactone may mitigate hypertrichosis. The study highlights the potential of combination therapy to address hypertrichosis and calls for larger studies to confirm these findings.

BACKGROUND/UNASSIGNED:To elucidate the sociodemographic and study factors involved in enrollment in the Traumatic Brain Injury Model System (TBIMS) database, this study examined the effect of a variety of variables on enrollment at a local TBIMS center. METHODS/UNASSIGNED:A sample of 654 individuals from the local TBIMS center was studied examining enrollment by age, gender, race, ethnicity, homelessness status at date of injury, history of homelessness, health insurance status, presence of social support, primary language, consenting in hospital or after discharge, and the need for an interpreter. Binary logistic regression was conducted to identify variables that predict center-based enrollment into TBIMS. RESULTS/UNASSIGNED: < 0.01). Here, OR denotes the odds ratio estimate from a logistic regression model and P denotes the corresponding p-value. CONCLUSIONS/UNASSIGNED:These results can be useful in driving enrollment strategies at this center for other similar TBI research, and to contribute a representative TBI sample to the national database.

We propose and study a simple and innovative non-parametric approach to estimate the age-of-onset distribution for a disease from a cross-sectional sample of the population that includes individuals with prevalent disease. First, we estimate the joint distribution of two event times, the age of disease onset and the survival time after disease onset. We accommodate that individuals had to be alive at the time of the study by conditioning on their survival until the age at sampling. We propose a computationally efficient expectation-maximization (EM) algorithm and derive the asymptotic properties of the resulting estimates. From these joint probabilities we then obtain non-parametric estimates of the age-at-onset distribution by marginalizing over the survival time after disease onset to death. The method accommodates categorical covariates and can be used to obtain unbiased estimates of the covariate distribution in the source population. We show in simulations that our method performs well in finite samples even under large amounts of truncation for prevalent cases. We apply the proposed method to data from female participants in the Washington Ashkenazi Study to estimate the age-at-onset distribution of breast cancer associated with carrying BRCA1 or BRCA2 mutations.

Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence only include newly diagnosed cases. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control studies under parametric assumptions for the survival time after diagnosis. Here we propose and study methods to additionally use prospectively observed survival times from prevalent and incident cases to adjust logistic models for the time between diagnosis and sampling, the backward time, for prevalent cases. This adjustment yields unbiased odds-ratio estimates from case-control studies that include prevalent cases. We propose a computationally simple two-step generalized method-of-moments estimation procedure. First, we estimate the survival distribution assuming a semiparametric Cox model using an expectation-maximization algorithm that yields fully efficient estimates and accommodates left truncation for prevalent cases and right censoring. Then, we use the estimated survival distribution in an extension of the logistic model to three groups (controls, incident, and prevalent cases), to adjust for the survival bias in prevalent cases. In simulations, under modest amounts of censoring, odds-ratios from the two-step procedure were equally efficient as those estimated from a joint logistic and survival data likelihood under parametric assumptions. This indicates that utilizing the cases' prospective survival data lessens model dependencies and improves precision of association estimates for case-control studies with prevalent cases. We illustrate the methods by estimating associations between single nucleotide polymorphisms and breast cancer risk using controls, and incident and prevalent cases sampled from the US Radiologic Technologists Study cohort.